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Deep Learning with Python
"The first edition of Deep Learning with Python is one of the best books on the subject. The second edition made it even better." - Todd Cook The bestseller revised! Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You’ll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable. about the technologyMachine learning has made remarkable progress in recent years. We’ve gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sector about the bookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. what's insideDeep learning from first principlesImage-classification, imagine segmentation, and object detectionDeep learning for natural language processingTimeseries forecastingNeural style transfer, text generation, and image generation about the readerReaders need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. about the authorFrançois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.
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Learning Geospatial Analysis with Python
Harness the powerful Python programming language to navigate the realms of geographic information systems, remote sensing, topography, and more, while embracing a guiding framework for effective geospatial analysisKey FeaturesCreate GIS solutions using the new features introduced in Python 3.10Explore a range of GIS tools and libraries, including PostGIS, QGIS, and PROJIdentify the tools and resources that best align with your specific needsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGeospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.What you will learnAutomate geospatial analysis workflows using PythonUnderstand the different formats in which geospatial data is availableUnleash geospatial tech tools to create stunning visualizationsCreate thematic maps with Python tools such as PyShp, OGR, and the Python Imaging LibraryBuild a geospatial Python toolbox for analysis and application developmentUnlock remote sensing secrets, detect changes, and process imageryLeverage ChatGPT for solving Python geospatial solutionsApply geospatial analysis to real-time data tracking and storm chasingWho this book is forThis book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.
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Practical Docker with Python
Learn the fundamentals of containerization and get acquainted with Docker. This second edition builds upon the foundation of the first book by revising all the chapters, updating the commands, code, and examples to meet the changes in Docker. It also introduces a new chapter on setting up your application for production deployment and breaks down terminologies like Dockerfile and Docker volumes while taking you on a guided tour of building a telegram bot using Python.You'll start with a brief history of how containerization has changed over the years. Next, we look at how to install (including using the new WSL2 mode) and get started with Docker. The next couple of chapters will focus on understanding the Dockerfile, including the structure and the core instructions used in building a Docker image. You'll also see how to distribute Docker images using Docker hub and other private registries. From there, you'll look at using Docker volumes for persisting data. Then learn how to run multi-container applications with Docker compose and learn inter-container networking works with Docker networks. Finally, you'll look at how to prepare a containerized application for production deployments. Throughout the book you'll apply the techniques learned through the chapters by building a Telegram messenger Chatbot and see how much easier Docker makes it possible to build, release, contribute and distribute an application. In addition, the book shows how optimize the Docker images for production servers by using multi-stage builds and improve the reliability of your services by using health checks and restart policies. Practical Docker with Python will break down terminologies like Dockerfile and Docker volumes, and take you on a guided tour of building a telegram bot using Python. What You'll LearnCompare the difference between containerization and virtualizationUnderstand the Dockerfile and converting your application to Docker imageDefine and run multi-container applications with Docker composeReview data persistency with Docker volumesWho This Book Is ForBeginner and intermediate developers, DevOps practitioners who are looking improving their build and release workflow by containerizing applications as well as system administrators learning to implement DevOps principles.
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Electrodynamics Tutorials with Python Simulations
This book provides an accessible introduction to intermediate-level electrodynamics with computational approaches to complement a traditional mathematical treatment of the subject. It covers key topics in electrodynamics, such as electromagnetic fields, forces, potentials, and waves as well as Special Theory of Relativity.Through intuition-building examples and visualizations in the Python programming language, it helps readers to develop technical computing skills in numerical and symbolic calculations, modeling and simulations, and visualizations. Python is a highly readable and practical programming language, making this book appropriate for students without extensive programming experience.This book can serve as an electrodynamics textbook for undergraduate physics and engineering students in their second or third years, who are studying intermediate- or advanced-level electrodynamics and who want to learn techniques for scientific computing at the same time. This book will also appeal to computer science students who want to see how their computer programming skills may be applied to science, particularly to physics, without needing too much background physics knowledge.Key features● Major concepts in classical electrodynamics are introduced cohesively through computational and mathematical treatments.● Computational examples in Python programming language guide students on how to simulate and visualize electrodynamic principles and phenomena for themselves.Taejoon Kouh is a Professor of Nano and Electronic Physics at Kookmin University, Republic of Korea. He earned his B.A. in physics from Boston University and Sc.M. and Ph.D. degrees in physics from Brown University. After his study in Providence, RI, he returned to Boston, MA, and worked as a postdoctoral research associate in the Department of Aerospace and Mechanical Engineering at Boston University. He is a full faculty member in the Department of Nano and Electronic Physics at Kookmin University in Seoul, Korea, teaching and supervising undergraduate and graduate students. His current research involves the dynamics of nanoelectromechanical systems and the development of fast and reliable transduction methods and innovative applications based on tiny motion.Minjoon Kouh is a program scientist for a philanthropic initiative. He was a Professor of Physics and Neuroscience at Drew University, USA, where he taught more than 30 distinct types of courses. He holds Ph.D. and B.S. degrees in physics from MIT and an M.A. from UC Berkeley. He completed a postdoctoral research fellowship at the Salk Institute for Biological Studies in La Jolla, CA. His research includes computational modeling of the primate visual cortex, information-theoretic analysis of neural responses, machine learning, and pedagogical innovations in undergraduate science education.
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Hands-on Scikit-Learn for Machine Learning Applications
Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine.All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complex machine learning algorithms.Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python.What You'll LearnWork with simple and complex datasets common to Scikit-LearnManipulate data into vectors and matrices for algorithmic processingBecome familiar with the Anaconda distribution used in data scienceApply machine learning with Classifiers, Regressors, and Dimensionality ReductionTune algorithms and find the best algorithms for each datasetLoad data from and save to CSV, JSON, Numpy, and Pandas formatsWho This Book Is ForThe aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.
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Applied Natural Language Processing with Python
Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment.What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and GensimManipulate and preprocess raw text data in formats such as .txt and .pdfStrengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content.
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Applied Unsupervised Learning with Python
Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled dataKey FeaturesLearn how to select the most suitable Python library to solve your problemCompare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use themDelve into the applications of neural networks using real-world datasetsBook DescriptionUnsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python.What you will learnUnderstand the basics and importance of clusteringBuild k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packagesExplore dimensionality reduction and its applicationsUse scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris datasetEmploy Keras to build autoencoder models for the CIFAR-10 datasetApply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction dataWho this book is forThis course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.
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Building SPAs with Django and HTML Over the Wire
Discover how to construct real-time applications with Python and Django without the hassle of learning JavaScriptKey FeaturesLearn to put together an SPA using Python and very little JavaScriptCreate WebSocket communication between the backend and frontendBuild a real-world project with Django using the techniques provided in this bookBook DescriptionThe HTML over WebSockets approach simplifies single-page application (SPA) development and lets you bypass learning a JavaScript rendering framework such as React, Vue, or Angular, moving the logic to Python. This web application development book provides you with all the Django tools you need to simplify your developments with real-time results.You'll learn state-of-the-art WebSocket techniques to realize real-time applications with minimal reliance on JavaScript. This book will also show you how to create a project with Docker from the ground up, test it, and deploy it on a server. You'll learn how to create a project, add Docker, and discover development libraries, Django channels, and bidirectional communication, and from then, on you'll create real projects of all kinds using HTML over WebSockets as a chat app or a blog with real-time comments. In addition, you'll modernize your development techniques by moving from using an SSR model to creating web pages using WebSockets over HTML. With Django, you'll be able to create SPAs with professional real-time projects where the logic is in Python.By the end of this Django book, you'll be able to build real-time applications, as well as gaining a solid understanding of WebSockets with Django.What you will learnExplore real-time site realizationUnderstand the proper use of Django channelsFind out how to set up Docker with DjangoDiscover how to use a JavaScript framework such as StimulusManage a database asynchronously in DjangoBring dynamic rendering logic to the backendWho this book is forThis book is for developers looking to build applications where they want to bring logic to the backend, learn WebSockets, and not depend on JavaScript heavily to create a single-page application. Basic knowledge of HTML and Python and familiarity with basic web development concepts is expected.
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Recurrent Neural Networks with Python Quick Start Guide
Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook DescriptionDevelopers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling.Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood.After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field.What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is forThis book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.
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Speed Up Your Python with Rust
Discover how to inject your code with highly performant Rust features to develop fast and memory-safe applicationsKey FeaturesLearn to implement Rust in a Python system without altering the entire systemWrite safe and efficient Rust code as a Python developer by understanding the essential features of RustBuild Python extensions in Rust by using Python NumPy modules in your Rust codeBook DescriptionPython has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. Rust, on the other hand, provides memory safety without using a garbage collector, which means that with its low memory footprint, you can build high-performant and secure apps relatively easily. However, rewriting everything in Rust can be expensive and risky as there might not be package support in Rust for the problem being solved. This is where Python bindings and pip come in.This book will help you, as a Python developer, to start using Rust in your Python projects without having to manage a separate Rust server or application. Seeing as you'll already understand concepts like functions and loops, this book covers the quirks of Rust such as memory management to code Rust in a productive and structured manner. You'll explore the PyO3 crate to fuse Rust code with Python, learn how to package your fused Rust code in a pip package, and then deploy a Python Flask application in Docker that uses a private Rust pip module. Finally, you'll get to grips with advanced Rust binding topics such as inspecting Python objects and modules in Rust.By the end of this Rust book, you'll be able to develop safe and high-performant applications with better concurrency support.What you will learnExplore the quirks of the Rust programming language that a Python developer needs to understand to code in RustUnderstand the trade-offs for multiprocessing and thread safety to write concurrent codeBuild and manage a software project with cargo and cratesFuse Rust code with Python so that Python can import and run Rust codeDeploy a Python Flask application in Docker that utilizes a private Rust pip moduleInspect and create your own Python objects in RustWho this book is forThis book is for Python developers who want to speed up their Python code with Rust and implement Rust in a Python system without altering the entire system. You'll be able to learn about all topics relating to Rust programming. Basic knowledge of Python is required to get the most out of this book.
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Linear Algebra and Its Applications with R
This book developed from the need to teach a linear algebra course to students focused on data science and bioinformatics programs. These students tend not to realize the importance of linear algebra in applied sciences, since traditional linear algebra courses tend to cover mathematical contexts but not the computational aspect of linear algebra or its applications to data science and bioinformatics. The author presents the topics in a traditional course, yet offers lectures as well as lab exercises on simulated and empirical data sets. This textbook provides students a theoretical basis which can then be applied to the practical R and Python problems, providing the tools needed for real-world applications.Each section starts with working examples to demonstrate how tools from linear algebra can help solve problems in applied sciences. These exercises start from easy computations, such as computing determinants of matrices, to practical applications on simulated and empirical data sets with R so that students learn how to get started with R, along with computational examples in each section, and then students learn how to apply what they've learned to problems in applied sciences.This book is designed from first principles to demonstrate the importance of linear algebra through working computational examples with R and Python, including tutorials on how to install R in the Appendix. If a student has never seen R, they can get started without any additional help.Since Python is one of the most popular languages in data science, optimization, and computer science, code supplements are available for students who feel more comfortable with Python. R is used primarily for computational examples to develop students’ practical computational skills.About the Author:Dr. Ruriko Yoshida is an Associate Professor of Operations Research at the Naval Postgraduate School. She received her PhD in Mathematics from the University of California, Davis. Her research topics cover a wide variety of areas: applications of algebraic combinatorics to statistical problems such as statistical learning on non-Euclidean spaces, sensor networks, phylogenetics, and phylogenomics. She teaches courses in statistics, stochastic models, probability, and data science.
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Machine Learning for Decision Sciences with Case Studies in Python
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning.This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
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Scaling Python with Ray
Serverless computing enables developers to concentrate solely on their applications rather than worry about where they've been deployed. With the Ray general-purpose serverless implementation in Python, programmers and data scientists can hide servers, implement stateful applications, support direct communication between tasks, and access hardware accelerators.In this book, authors Holden Karau and Boris Lublinsky show you how to scale existing Python applications and pipelines, allowing you to stay in the Python ecosystem while avoiding single points of failure and manual scheduling. If your data processing has grown beyond what a single computer can handle, this book is for you.Written by experienced software architecture practitioners, Scaling Python with Ray is ideal for software architects and developers eager to explore successful case studies and learn more about decision and measurement effectiveness. This book covers distributed processing (the pure Python implementation of serverless) and shows you how to:Implement stateful applications with Ray actorsBuild workflow management in RayUse Ray as a unified platform for batch and streamingImplement advanced data processing with RayApply microservices with Ray platformImplement reliable Ray applications
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Pro Android Python with SL4A
Pro Android Python with SL4A is for programmers and hobbyists who want to write apps for Android devices without having to learn Java first. Paul Ferrill leads you from installing the Scripting Layer for Android (SL4A) to writing small scripts, to more complicated and interesting projects, and finally to uploading and packaging your programs to an Android device.Android runs scripts in many scripting languages, but Python, Lua, and Beanshell are particularly popular. Most programmers know more than one programming language, so that they have the best tool for whatever task they want to accomplish. Pro Android Python with SL4A explores the world of Android scripting by introducing you to the most important open-source programming languages that are available on Android-based hardware. Pro Android Python with SL4A starts by exploring the Android software development kit and then shows you how to set up an Eclipse-based Android development environment. You then approach the world of Android programming by using Beanshell, which runs on the Dalvik, and learning how to write small programs to administer an Android device.Next, discover how Lua, a lightweight language perfectly suited for scripting on smaller devices, can work with Android. Lua can be used for small but important tasks, like SMS encryption and synchronizing photos with flickr. Last, but certainly not least, you will discover the world of Python scripting for SL4A, and the power contained within the full range of Python modules that can combine with the Android SDK. You'll learn to write small location-aware apps to get you started, but by the end of this book, you'll find yourself writing fully GUI-fied applications running on the Android desktop!Pro Android Python with SL4A is rounded out with a chapter on distributing and packaging scripts, a skill that you'll find very useful as you reach out to a wider audience with your programs.
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Hands-On Transfer Learning with Python
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.What you will learnSet up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL modelsExplore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functionsGet to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLPWho this book is forHands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
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Adaptive Machine Learning Algorithms with Python
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use.Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment.What You Will LearnApply adaptive algorithms to practical applications and examplesUnderstand the relevant data representation features and computational models for time-varying multi-dimensional dataDerive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real dataSpeed up your algorithms and put them to use on real-world stationary and non-stationary dataMaster the applications of adaptive algorithms on critical edge device computation applicationsWho This Book Is ForMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.
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Supervised Machine Learning with Python
Teach your machine to think for itself!Key FeaturesDelve into supervised learning and grasp how a machine learns from dataImplement popular machine learning algorithms from scratch, developing a deep understanding along the wayExplore some of the most popular scientific and mathematical libraries in the Python languageBook DescriptionSupervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood.This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You’ll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning.By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems.What you will learnCrack how a machine learns a concept and generalize its understanding to new dataUncover the fundamental differences between parametric and non-parametric modelsImplement and grok several well-known supervised learning algorithms from scratchWork with models in domains such as ecommerce and marketingExpand your expertise and use various algorithms such as regression, decision trees, and clusteringBuild your own models capable of making predictionsDelve into the most popular approaches in deep learning such as transfer learning and neural networksWho this book is forThis book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming—and some fundamental knowledge of supervised learning—are expected.
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Handbook of Mathematical Models with Python
Master the art of mathematical modeling through practical examples, use cases, and machine learning techniquesKey FeaturesGain a profound understanding of various mathematical models that can be integrated with machine learningLearn how to implement optimization algorithms to tune machine learning modelsBuild optimal solutions for practical use casesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMathematical modeling is the art of transforming a business problem into a well-defined mathematical formulation. Its emphasis on interpretability is particularly crucial when deploying a model to support high-stake decisions in sensitive sectors like pharmaceuticals and healthcare.Through this book, you’ll gain a firm grasp of the foundational mathematics underpinning various machine learning algorithms. Equipped with this knowledge, you can modify algorithms to suit your business problem. Starting with the basic theory and concepts of mathematical modeling, you’ll explore an array of mathematical tools that will empower you to extract insights and understand the data better, which in turn will aid in making optimal, data-driven decisions. The book allows you to explore mathematical optimization and its wide range of applications, and concludes by highlighting the synergetic value derived from blending mathematical models with machine learning.Ultimately, you’ll be able to apply everything you’ve learned to choose the most fitting methodologies for the business problems you encounter.What you will learnUnderstand core concepts of mathematical models and their relevance in solving problemsExplore various approaches to modeling and learning using PythonWork with tested mathematical tools to gather meaningful insightsBlend mathematical modeling with machine learning to find optimal solutions to business problemsOptimize ML models built with business data, apply them to understand their impact on the business, and address critical questionsApply mathematical optimization for data-scarce problems where the objective and constraints are knownWho this book is forIf you are a budding data scientist seeking to augment your journey with mathematics, this book is for you. Researchers and R&D scientists will also be able to harness the concepts covered to their full potential. To make the best use of this book, a background in linear algebra, differential equations, basics of statistics, data types, data structures, and numerical algorithms will be useful.
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Hands-on Signal Analysis with Python
This book provides the tools for analyzing data in Python: different types of filters are introduced and explained, such as FIR-, IIR- and morphological filters, as well as their application to one- and two-dimensional data. The required mathematics are kept to a minimum, and numerous examples and working Python programs are included for a quick start. The goal of the book is to enable also novice users to choose appropriate methods and to complete real-world tasks such as differentiation, integration, and smoothing of time series, or simple edge detection in images. An introductory section provides help and tips for getting Python installed and configured on your computer. More advanced chapters provide a practical introduction to the Fourier transform and its applications such as sound processing, as well as to the solution of equations of motion with the Laplace transform. A brief excursion into machine learning shows the powerful tools that are available with Python. This book also provides tips for an efficient programming work flow: from the use of a debugger for finding mistakes, code-versioning with git to avoid the loss of working programs, to the construction of graphical user interfaces (GUIs) for the visualization of data. Working, well-documented Python solutions are included for all exercises, and IPython/Jupyter notebooks provide additional help to get people started and outlooks for the interested reader.
R 2,462.02
Building Cross-Platform GUI Applications with Fyne
Understand how to use the Fyne toolkit to build exciting apps for a range of devices and deploy them effectivelyKey FeaturesLearn how to use standard widgets, dialogs, and layouts as well as how to build your ownUnderstand how to develop an app and package and distribute it to different operating systems and app storesExplore the design principles and vision of the Fyne toolkit and how that may align with your projectBook DescriptionThe history of graphical application development is long and complicated, with various development challenges that persist to this day. The mix of technologies involved and the need to use different programming languages led to a very steep learning curve for developers looking to build applications across multiple platforms. In Building Cross-Platform GUI Applications with Fyne, you'll understand how the Go language, when paired with a modern graphical toolkit such as Fyne, can overcome these issues and make application development much easier. To provide an easy-to-use framework for cross-platform app development, the Fyne project offers many graphical concepts and design principles that are outlined throughout this book. By working through five example projects, you'll learn how to build apps effectively, focusing on each of the main areas, including the canvas, layouts, file handling, widgets, data binding, and themes. The book will also show you how the completed applications can then be run on your desktop computer, laptop, and smartphone. After completing these projects, you will discover how to prepare applications for release and distribute them to platform marketplaces and app stores. By the end of this book, you'll be able to create cross-platform graphical applications with visually appealing user interfaces and concise code.What you will learnBecome well-versed with the history of GUI development and how Fyne and the Golang programming language make it easierExplore how the Fyne toolkit is architected and the various modules are providedDiscover how Fyne apps can be tested and constructed using best practicesConstruct five complete applications and deploy them to your devicesCustomize the design of your apps by extending widgets and themesUnderstand the separation and presentation of data and how to test and build applications that present dynamic dataWho this book is forThis Fyne-Golang GUI book is for developers from any background who are looking to build cross-platform applications with a modern toolkit. It will also be useful for Go developers who are looking to explore graphical apps and GUI developers looking for a new toolkit for cross-platform development. Basic knowledge of Graphical User Interface (GUI) development is assumed (although a brief history is also included in the book). The book also features a short introduction to the Go language as a quick refresher.
R 1,498.00
Computational Frameworks for Political and Social Research with Python
This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.
R 4,260.30
Artificial Intelligence Programming with Python
A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learningExpansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learningPractical AI and Python “cheat sheet” quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.
R 958.00
R 808.06
Mastering OpenCV 4 with Python
Create advanced applications with Python and OpenCV, exploring the potential of facial recognition, machine learning, deep learning, web computing and augmented reality.Key FeaturesDevelop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4) and PythonApply machine learning and deep learning techniques with TensorFlow and KerasDiscover the modern design patterns you should avoid when developing efficient computer vision applicationsBook DescriptionOpenCV is considered to be one of the best open source computer vision and machine learning software libraries. It helps developers build complete projects in relation to image processing, motion detection, or image segmentation, among many others. OpenCV for Python enables you to run computer vision algorithms smoothly in real time, combining the best of the OpenCV C++ API and the Python language.In this book, you'll get started by setting up OpenCV and delving into the key concepts of computer vision. You'll then proceed to study more advanced concepts and discover the full potential of OpenCV. The book will also introduce you to the creation of advanced applications using Python and OpenCV, enabling you to develop applications that include facial recognition, target tracking, or augmented reality. Next, you'll learn machine learning techniques and concepts, understand how to apply them in real-world examples, and also explore their benefits, including real-time data production and faster data processing. You'll also discover how to translate the functionality provided by OpenCV into optimized application code projects using Python bindings. Toward the concluding chapters, you'll explore the application of artificial intelligence and deep learning techniques using the popular Python libraries TensorFlow, and Keras.By the end of this book, you'll be able to develop advanced computer vision applications to meet your customers' demands.What you will learnHandle files and images, and explore various image processing techniquesExplore image transformations, including translation, resizing, and croppingGain insights into building histogramsBrush up on contour detection, filtering, and drawingWork with Augmented Reality to build marker-based and markerless applicationsWork with the main machine learning algorithms in OpenCVExplore the deep learning Python libraries and OpenCV deep learning capabilitiesCreate computer vision and deep learning web applicationsWho this book is forThis book is designed for computer vision developers, engineers, and researchers who want to develop modern computer vision applications. Basic experience of OpenCV and Python programming is a must.
R 1,661.00
Computational Frameworks for Political and Social Research with Python
This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions.
R 2,734.60
Distributed Machine Learning with Python
Build and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloudKey FeaturesAccelerate model training and interference with order-of-magnitude time reductionLearn state-of-the-art parallel schemes for both model training and servingA detailed study of bottlenecks at distributed model training and serving stagesBook DescriptionReducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.What you will learnDeploy distributed model training and serving pipelinesGet to grips with the advanced features in TensorFlow and PyTorchMitigate system bottlenecks during in-parallel model training and servingDiscover the latest techniques on top of classical parallelism paradigmExplore advanced features in Megatron-LM and Mesh-TensorFlowUse state-of-the-art hardware such as NVLink, NVSwitch, and GPUsWho this book is forThis book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.
R 1,433.00
Building Production-Grade Web Applications with Supabase
Craft resilient web applications with Supabase by leveraging advanced features such as authentication, data and user management, and seamless AI integration using its powerful Postgres infrastructureKey FeaturesLearn how to integrate Supabase and Next.js to create powerful and scalable web appsExplore real-world scenarios with a multi-tenant ticket systemMaster real-time data handling, secure file storage, and application security enhancement, while discovering the full potential of the database beyond holding dataPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDiscover the powerful capabilities of Supabase, the cutting-edge, open-source platform flipping the script on backend architecture. Guided by David Lorenz, a battle-tested software architect with over two decades of development experience, this book will transform the way you approach your projects and make you a Supabase expert. In this comprehensive guide, you'll build a secure, production-grade multi-tenant ticket system, seamlessly integrated with Next.js. You’ll build essential skills for effective data manipulation, authentication, and file storage, as well as master Supabase's advanced capabilities including automating tasks with cron scheduling, performing similarity searches with artificial intelligence, testing your database, and leveraging real-time updates. By the end of the book, you'll have a deeper understanding of the platform and be able to confidently utilize Supabase in your own web applications, all thanks to David's excellent expertise.What you will learnExplore essential features for effective web app developmentHandle user registration, login/logout processes, and user metadataNavigate multi-tenant applications and understand the potential pitfalls and best practicesDiscover how to implement real-time functionalityFind out how to upload, download, and manipulate filesExplore preventive measures against data manipulation and security breaches, ensuring robust web app securityIncrease efficiency and streamline task automation through personalized email communication, webhooks, and cron jobsWho this book is forThis book is for developers looking for a hassle-free, universal solution to building robust apps using Supabase and its integration libraries. While a basic understanding of JavaScript is useful, it’s not essential as the book focuses on Supabase for creating high-performance web apps using Next.js. Experienced professionals from non-JavaScript backgrounds will find this book useful. Familiarity with Postgres, although helpful, is not mandatory as the book explains all the SQL statements used.
R 1,303.00
Learn Programming in Python with Cody Jackson
Kick-start your development journey with this end-to-end guide that covers Python programming fundamentals along with application developmentKey FeaturesGain a solid understanding of Python programming with coverage of data structures and Object-Oriented Programming (OOP)Design graphical user interfaces for desktops with libraries such as Kivy and TkinterWrite elegant, reusable, and efficient code Book DescriptionPython is a cross-platform language used by organizations such as Google and NASA. It lets you work quickly and efficiently, allowing you to concentrate on your work rather than the language. Based on his personal experiences when learning to program, Learn Programming in Python with Cody Jackson provides a hands-on introduction to computer programming utilizing one of the most readable programming languages–Python. It aims to educate readers regarding software development as well as help experienced developers become familiar with the Python language, utilizing real-world lessons to help readers understand programming concepts quickly and easily. The book starts with the basics of programming, and describes Python syntax while developing the skills to make complete programs. In the first part of the book, readers will be going through all the concepts with short and easy-to-understand code samples that will prepare them for the comprehensive application built in parts 2 and 3. The second part of the book will explore topics such as application requirements, building the application, testing, and documentation. It is here that you will get a solid understanding of building an end-to-end application in Python. The next part will show you how to complete your applications by converting text-based simulation into an interactive, graphical user interface, using a desktop GUI framework. After reading the book, you will be confident in developing a complete application in Python, from program design to documentation to deployment.What you will learnUse the interactive shell for prototyping and code execution, including variable assignment Deal with program errors by learning when to manually throw exceptions Employ exceptions for code management Enhance code by utilizing Python's built-in shortcuts to improve efficiency and make coding easier Interact with files and package Python data for network transfer or storage Understand how tests drive code writing, and vice versaExplore the different frameworks that are available for GUI developmentWho this book is forLearn Programming in Python with Cody Jackson is for beginners or novice programmers who have no programming background and wish to take their first step in software development. This book will also be beneficial for intermediate programmers and will provide deeper insights into effective coding practices in Python.
R 1,335.00
Dancing with Python
Develop skills in Python and Quantum Computing by implementing exciting algorithms, mathematical functions, classical searching, data analysis, plotting data, machine learning techniques, and quantum circuits.Key FeaturesCreate quantum circuits and algorithms using Qiskit and run them on quantum computing hardware and simulatorsLearn the Pythonic way to write elegant and efficient codeDelve into Python's advanced features, including machine learning, analyzing data, and searchingBook DescriptionDancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files.The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning.By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way.What you will learnExplore different quantum gates and build quantum circuits with Qiskit and PythonWrite succinct code the Pythonic way using magic methods, iterators, and generatorsAnalyze data, build basic machine learning models, and plot the resultsSearch for information using the quantum Grover Search AlgorithmOptimize and test your code to run efficientlyWho this book is forThe book will help you get started with coding for Python and Quantum Computing. Basic familiarity with algebra, geometry, trigonometry, and logarithms is required as the book does not cover the detailed mathematics and theory of quantum computing. You can check out the author’s Dancing with Qubits book, also published by Packt, for an approachable and comprehensive introduction to quantum computing.
R 1,661.00
R 611.00
Developing Graphics Frameworks with Python and OpenGL
Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds.You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results.
R 6,118.00
Kivy: Interactive Applications in Python
into the world of Kivy.This book is aimed at Python developers who are familiar with Python and have a good understanding of concepts like inheritance, classes, and instances. No previous experience of Kivy is required, though some knowledge of event handling, scheduling, and user interfaces, in general, would boost your learning.
R 1,335.00
Programming with Python
Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency.
R 5,045.66
Interpretable Machine Learning with Python 2e
A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesInterpret real-world data, including cardiovascular disease data and the COMPAS recidivism scoresBuild your interpretability toolkit with global, local, model-agnostic, and model-specific methodsAnalyze and extract insights from complex models from CNNs to BERT to time series modelsBook DescriptionInterpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting and machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.What you will learnProgress from basic to advanced techniques, such as causal inference and quantifying uncertaintyBuild your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformersUse monotonic and interaction constraints to make fairer and safer modelsUnderstand how to mitigate the influence of bias in datasetsLeverage sensitivity analysis factor prioritization and factor fixing for any modelDiscover how to make models more reliable with adversarial robustnessWho this book is forThis book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.
R 1,693.00
Artificial Intelligence with Python
New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more.Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook DescriptionArtificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data.Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is forThe intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.
R 1,889.00
R 611.00
Linear Algebra and Its Applications with R
This book developed from the need to teach a linear algebra course to students focused on data science and bioinformatics programs. These students tend not to realize the importance of linear algebra in applied sciences, since traditional linear algebra courses tend to cover mathematical contexts but not the computational aspect of linear algebra or its applications to data science and bioinformatics. The author presents the topics in a traditional course, yet offers lectures as well as lab exercises on simulated and empirical data sets. This textbook provides students a theoretical basis which can then be applied to the practical R and Python problems, providing the tools needed for real-world applications.Each section starts with working examples to demonstrate how tools from linear algebra can help solve problems in applied sciences. These exercises start from easy computations, such as computing determinants of matrices, to practical applications on simulated and empirical data sets with R so that students learn how to get started with R, along with computational examples in each section, and then students learn how to apply what they've learned to problems in applied sciences.This book is designed from first principles to demonstrate the importance of linear algebra through working computational examples with R and Python, including tutorials on how to install R in the Appendix. If a student has never seen R, they can get started without any additional help.Since Python is one of the most popular languages in data science, optimization, and computer science, code supplements are available for students who feel more comfortable with Python. R is used primarily for computational examples to develop students' practical computational skills.About the Author: Dr. Ruriko Yoshida is an Associate Professor of Operations Research at the Naval Postgraduate School. She received her PhD in Mathematics from the University of California, Davis. Her research topics cover a wide variety of areas: applications of algebraic combinatorics to statistical problems such as statistical learning on non-Euclidean spaces, sensor networks, phylogenetics, and phylogenomics. She teaches courses in statistics, stochastic models, probability, and data science.
R 3,453.17
Blueprint for Production-Ready Web Applications
Go from setting up your production environment, to building an app, to deploying it to the web using industry best practices along the wayKey FeaturesFollow a blueprint to build production-ready apps that can be adapted and tailored to your requirementsLearn how to combine a React frontend with a Quart backend, and run them in Docker on AWSAdopt industry best practices that can be used in your personal as well as work projectsBook DescriptionA Blueprint for Production-Ready Web Applications will help you expand upon your coding knowledge and teach you how to create a complete web application. Unlike other guides that focus solely on a singular technology or process, this book shows you how to combine different technologies and processes as needed to meet industry standards.You'll begin by learning how to set up your development environment, and use Quart and React to create the backend and frontend, respectively. This book then helps you get to grips with managing and validating accounts, structuring relational tables, and creating forms to manage data. As you progress through the chapters, you'll gain a comprehensive understanding of web application development by creating a to-do app, which can be used as a base for your future projects. Finally, you'll find out how to deploy and monitor your application, along with discovering advanced concepts such as managing database migrations and adding multifactor authentication.By the end of this web development book, you'll be able to apply the lessons and industry best practices that you've learned to both your personal and work projects, allowing you to further develop your coding portfolio.What you will learnSet up an optimum development environment for building web appsCreate a working backend Quart app that can be tailored to suit your needsBuild a user management system with passwords and authenticationBuild a single-page application with React Router and FormikDeploy your app to AWS and understand the importance of monitoringDiscover advanced concepts such as managing database migrationsWho this book is forThis book is for software engineers like recent computer science or bootcamp graduates who already know how to program and want to learn how to build an app by following standard industry processes such as continuous integration and continuous deployment (CI/CD). Working knowledge of TypeScript/JavaScript, Python, HTML, CSS, and SQL is needed. You don't, however, need prior experience with Quart, React, AWS, and all other specific technologies and processes, as they will be introduced in the book.
R 1,270.00
Machine Learning with Python Cookbook
This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems all the way from loading data to training models and leveraging neural networks.Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.You'll find recipes for:Vectors, matrices, and arraysWorking with data from CSV, JSON, SQL, databases, cloud storage, and other sourcesHandling numerical and categorical data, text, images, and dates and timesDimensionality reduction using feature extraction or feature selectionModel evaluation and selectionLinear and logical regression, trees and forests, and k-nearest neighborsSupport vector machines (SVM), naive Bayes, clustering, and tree-based modelsSaving and loading trained models from multiple frameworks
R 1,824.00
Mastering Social Media Mining with Python
Acquire and analyze data from all corners of the social web with PythonAbout This Book• Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide• Use this easy-to-follow, step-by-step guide to apply analytics to complicated and messy social data• This is your one-stop solution to fetching, storing, analyzing, and visualizing social media dataWho This Book Is ForThis book is for intermediate Python developers who want to engage with the use of public APIs to collect data from social media platforms and perform statistical analysis in order to produce useful insights from data. The book assumes a basic understanding of the Python Standard Library and provides practical examples to guide you toward the creation of your data analysis project based on social data.What You Will Learn• Interact with a social media platform via their public API with Python• Store social data in a convenient format for data analysis• Slice and dice social data using Python tools for data science• Apply text analytics techniques to understand what people are talking about on social media• Apply advanced statistical and analytical techniques to produce useful insights from data• Build beautiful visualizations with web technologies to explore data and present data productsIn DetailYour social media is filled with a wealth of hidden data – unlock it with the power of Python. Transform your understanding of your clients and customers when you use Python to solve the problems of understanding consumer behavior and turning raw data into actionable customer insights.This book will help you acquire and analyze data from leading social media sites. It will show you how to employ scientific Python tools to mine popular social websites such as Facebook, Twitter, Quora, and more. Explore the Python libraries used for social media mining, and get the tips, tricks, and insider insight you need to make the most of them. Discover how to develop data mining tools that use a social media API, and how to create your own data analysis projects using Python for clear insight from your social data.Style and approachThis practical, hands-on guide will help you learn everything you need to perform data mining for social media. Throughout the book, we take an example-oriented approach to use Python for data analysis and provide useful tips and tricks that you can use in day-to-day tasks.
R 1,661.00
OpenCV: Computer Vision Projects with Python
Get savvy with OpenCV and actualize cool computer vision applicationsAbout This Book* Use OpenCV's Python bindings to capture video, manipulate images, and track objects* Learn about the different functions of OpenCV and their actual implementations.* Develop a series of intermediate to advanced projects using OpenCV and PythonWho This Book Is ForThis learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV's application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV.What You Will Learn* Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or Ubuntu* Apply "curves" and other color transformations to simulate the look of old photos, movies, or video games* Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image* Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor* Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques* Detect and recognize street signs using a cascade classifier and support vector machines (SVMs)* Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMs* Strengthen your OpenCV2 skills and learn how to use new OpenCV3 featuresIn DetailOpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3's Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images , and building an augmented reality application, Finally, we'll move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:* OpenCV Computer Vision with Python by Joseph Howse * OpenCV with Python By Example by Prateek Joshi* OpenCV with Python Blueprints by Michael BeyelerStyle and approachThis course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3's Python API, and develop superb computer vision applications. Through this
R 2,605.00
Getting Started with Python Data Analysis
Learn to use powerful Python libraries for effective data processing and analysisAbout This Book• Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib• Create, manipulate, and analyze your data to extract useful information to optimize your system• A hands-on guide to help you learn data analysis using PythonWho This Book Is ForIf you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.What You Will Learn• Understand the importance of data analysis and get familiar with its processing steps• Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis• Create effective visualizations to present your data using Matplotlib• Process and analyze data using the time series capabilities of Pandas• Interact with different kind of database systems, such as file, disk format, Mongo, and Redis• Apply the supported Python package to data analysis applications through examples• Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python libraryIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.With this book, we will get you started with Python data analysis and show you what its advantages are.The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn. Style and approachThis is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.
R 1,335.00
Artificial Intelligence with Python
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around youAbout This Book• Step into the amazing world of intelligent apps using this comprehensive guide• Enter the world of Artificial Intelligence, explore it, and create your own applications• Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no timeWho This Book Is ForThis book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks.What You Will Learn• Realize different classification and regression techniques• Understand the concept of clustering and how to use it to automatically segment data• See how to build an intelligent recommender system• Understand logic programming and how to use it• Build automatic speech recognition systems• Understand the basics of heuristic search and genetic programming• Develop games using Artificial Intelligence• Learn how reinforcement learning works• Discover how to build intelligent applications centered on images, text, and time series data• See how to use deep learning algorithms and build applications based on itIn DetailArtificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications.During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide!Style and approachThis highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
R 1,889.00
