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Hands-On Mathematics for Deep Learning
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook DescriptionMost programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is forThis book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.
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Functional Python Programming
Python isn't all about object-oriented programming. Discover a valuable way of thinking about code design through a function-first approach – and learn when you need to use it. Now with detailed exercises at the end of every chapter!Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesLearn how, when, and why to adopt functional elements in your projectsExplore the Python modules essential to functional programming, like itertools and functoolsRevised to cover new features of Python 3.10, exercises at the end of every chapter, and moreBook DescriptionNot enough developers understand the benefits of functional programming, or even what it is. Author Steven Lott demystifies the approach, teaching you how to improve the way you code in Python and make gains in memory use and performance. Starting from the fundamentals, this book shows you how to apply functional thinking and techniques in a range of scenarios, with examples centered around data cleaning and exploratory data analysis. You'll learn how to use generator expressions, list comprehensions, and decorators to your advantage. You don't have to abandon object-oriented design completely, though – you'll also see how Python's native object-orientation is used in conjunction with functional programming techniques. By the end of this book, you'll be well versed in the essential functional programming features of Python, and understand why and when functional thinking helps. You'll also have all the tools you need to pursue any additional functional topics that are not part of the Python language.What you will learnUse Python's libraries to avoid the complexities of state-changing classesLeverage built-in higher-order functions to avoid rewriting common algorithmsWrite generator functions to create lazy processingDesign and implement decorators for functional compositionMake use of Python type annotations to describe parameters and results of functionsApply functional programming to concurrency and web servicesExplore the PyMonad library for stateful simulationsWho this book is forThe functional paradigm is very useful for programmers working in data science, but any Python developer who wants to create more reliable, succinct, and expressive code will have much to learn from this book.No prior knowledge of functional programming is required to get started, though Python programming knowledge is assumed. A running Python environment is essential.
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Python Programming in A Day
Python Programming In A Day 2nd Edition: Beginners Power Guide To Learning Python Programming From Scratch What exactly is python programming and is it really that complex? How is Python made so easy so the jargon is gone and you can have success programming within a day? Want to know all about interactive mode with mathematical operations? Variables? Strings? Mr. Key is going above and beyond with data types, programming modes and conditional statements Always wanted a step by step guide to begin a program building from start to finish? Look no further! Its time for you to take advantage of this easy programming guide so you can have immediate success in Python Programming! But Now!
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Python for Marketing Research and Analytics
This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.
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Python Programming for Biology
Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language.
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Python Programming
Maintaining a practical perspective, Python Programming: A Practical Approach acquaints you with the wonderful world of programming. The book is a starting point for those who want to learn Python programming. The backbone of any programming, which is the data structure and components such as strings, lists, etc., have been illustrated with many examples and enough practice problems to instill a level of self-confidence in the reader.Drawing on knowledge gained directly from teaching Computer Science as a subject and working on a wide range of projects related to ML, AI, deep learning, and blockchain, the authors have tried their best to present the necessary skills for a Python programmer. Once the foundation of Python programming is built and the readers are aware of the exact structure, dimensions, processing, building blocks, and representation of data, they can readily take up their specific problems from the area of interest and solve them with the help of Python. These include, but are not limited to, operators, control flow, strings, functions, module processing, object-oriented programming, exception and file handling, multithreading, synchronization, regular expressions, and Python database programming.This book on Python programming is specially designed to keep readers busy with learning fundamentals and generates a sense of confidence by attempting the assignment problems. We firmly believe that explaining any particular technology deviates from learning the fundamentals of a programming language. This book is focused on helping readers attempt implementation in their areas of interest through the skills imparted through this book. We have attempted to present the real essence of Python programming, which you can confidently apply in real life by using Python as a tool.Salient Features Based on real-world requirements and solution. Simple presentation without avoiding necessary details of the topic. Executable programs on almost every topic. Plenty of exercise questions, designed to test readers’ skills and understanding.Purposefully designed to be instantly applicable, Python Programming: A Practical Approach provides implementation examples so that the described subject matter can be immediately implemented due to the well-known versatility of Python in handling different data types with ease.
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Python for Engineers and Scientists
The text focuses on the basics of Python programming fundamentals and introduction to present-day applications in technology and the upcoming state-of-art trends in a comprehensive manner. The text is based on Python 3.x and it covers the fundamentals of Python with object-oriented concepts having numerous worked-out examples. It provides a learning tool for the students of beginner level as well as for researchers of advanced level. Each chapter contains additional examples that explain the usage of methods/functions discussed in the chapter. It provides numerous programming examples along with their outputs.The book:Includes programming tips to highlight the important concepts and help readers avoid common programming errorsProvides programming examples along with their outputs to ensure the correctness and help readers in mastering the art of writing efficient Python programsContains MCQs with their answers; conceptual questions and programming questions; and solutions to some selected programming questions, for every chapterDiscusses applications like time zone converter and password generators at the endCovers fundamental of Python up to object oriented concepts including regular expressionThe book offers a simple and lucid treatment of concepts supported with illustrations for easy understanding, provides numerous programming examples along with their outputs, and includes programming tips to highlight the important concepts. It will be a valuable resource for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
R 6,118.00
Python for Linguists
Specifically designed for linguists, this book provides an introduction to programming using Python for those with little to no experience of coding. Python is one of the most popular and widely-used programming languages as it's also available for free and runs on any operating system. All examples in the text involve language data and can be adapted or used directly for language research. The text focuses on key language-related issues: searching, text manipulation, text encoding and internet data, providing an excellent resource for language research. More experienced users of Python will also benefit from the advanced chapters on graphical user interfaces and functional programming.
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Geocomputation with Python
Geocomputation with Python is a comprehensive resource for working with geographic data with the most popular programming language in the world. The book gives an overview of Python's capabilities for spatial data analysis, as well as dozens of worked-through examples covering the entire range of standard GIS operations. A unique selling point of the book is its cohesive and joined-up coverage of both vector and raster geographic data models and consistent learning curve. This book is an excellent starting point for those new to working with geographic data with Python, making it ideal for students and practitioners beginning their journey with Python.Key features:Showcases the integration of vector and raster datasets operations.Provides explanation of each line of code in the book to minimize surprises.Includes example datasets and meaningful operations to illustrate the applied nature of geographic research.Another unique feature is that this book is part of a wider community. Geocomputation with Python is a sister project of Geocomputation with R (Lovelace, Nowosad, and Muenchow 2019), a book on geographic data analysis, visualization, and modeling using the R programming language that has numerous contributors and an active community.The book teaches how to import, process, examine, transform, compute, and export spatial vector and raster datasets with Python, the most widely used language for data science and many other domains. Reading the book and running the reproducible code chunks within will make you a proficient user of key packages in the ecosystem, including shapely, geopandas, and rasterio. The book also demonstrates how to make use of dozens of additional packages for a wide range of tasks, from interactive map making to terrain modeling. Geocomputation with Python provides a firm foundation for more advanced topics, including spatial statistics, machine learning involving spatial data, and spatial network analysis, and a gateway into the vibrant and supportive community developing geographic tools in Python and beyond.
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Python Programming
This book explores Python's rich history, dynamic features, and its wide-ranging applications in web development, data science, and machine learning. From its installation process to interactive help, readers embark on a journey through Python's unique characteristics and its distinctions from other programming languages. It lays a solid foundation for beginners and seasoned programmers alike.Print edition not for sale in South Asia (India, Sri Lanka, Nepal, Bangladesh, Pakistan or Bhutan)
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Pythonic Programming
Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break.Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented.This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers.Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips.What You Need:You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.
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Julia 1.0 Programming
Enter the exciting world of Julia, a high-performance language for technical computingKey FeaturesLeverage Julia's high speed and efficiency for your applicationsWork with Julia in a multi-core, distributed, and networked environmentApply Julia to tackle problems concurrently and in a distributed environmentBook DescriptionThe release of Julia 1.0 is now ready to change the technical world by combining the high productivity and ease of use of Python and R with the lightning-fast speed of C++. Julia 1.0 programming gives you a head start in tackling your numerical and data problems. You will begin by learning how to set up a running Julia platform, before exploring its various built-in types. With the help of practical examples, this book walks you through two important collection types: arrays and matrices. In addition to this, you will be taken through how type conversions and promotions work. In the course of the book, you will be introduced to the homo-iconicity and metaprogramming concepts in Julia. You will understand how Julia provides different ways to interact with an operating system, as well as other languages, and then you'll discover what macros are. Once you have grasped the basics, you’ll study what makes Julia suitable for numerical and scientific computing, and learn about the features provided by Julia. By the end of this book, you will also have learned how to run external programs. This book covers all you need to know about Julia in order to leverage its high speed and efficiency for your applications.What you will learnSet up your Julia environment to achieve high productivityCreate your own types to extend the built-in type systemVisualize your data in Julia with plotting packagesExplore the use of built-in macros for testing and debugging, among other usesApply Julia to tackle problems concurrentlyIntegrate Julia with other languages such as C, Python, and MATLABWho this book is forJulia 1.0 Programming is for you if you are a statistician or data scientist who wants a crash course in the Julia programming language while building big data applications. A basic knowledge of mathematics is needed to understand the various methods that are used or created during the course of the book to exploit the capabilities that Julia is designed with.
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Python Programming Fundamentals
This easy-to-follow and classroom-tested textbook guides the reader through the fundamentals of programming with Python, an accessible language which can be learned incrementally. Features: incudes numerous examples and practice exercises throughout the text, with additional exercises, solutions and review questions at the end of each chapter; highlights the patterns which frequently appear when writing programs, reinforcing the application of these patterns for problem-solving through practice exercises; introduces the use of a debugger tool to inspect a program, enabling students to discover for themselves how programs work and enhance their understanding; presents the Tkinter framework for building graphical user interface applications and event-driven programs; provides instructional videos and additional information for students, as well as support materials for instructors, at an associated website.
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Challenging Programming in Python: A Problem Solving Perspective
This book aims to strengthen programming skills and foster creative thinking by presenting and solving 90 challenging problems. The book is intended for individuals with elementary, intermediate, and advanced Python programming skills who aspire to take their abilities to the next level. Additionally, the book is valuable for individuals interested in enhancing their creative thinking and logical reasoning skills. It is a self-instructional book meant to provide readers with the ability to solve challenging problems independently. The presented challenges are lucidly and succinctly expressed, facilitating readers to follow along and comprehend the problem-solving process. The challenges cover various fields, making it suitable for a wide range of individuals. The book is divided into eight chapters, beginning with an introduction in chapter one. The second chapter presents essential Python basics for programming challenging problems, while the subsequent chapters focus on specific types of challenges. These include math-based challenges in chapter three, number-based challenges in chapter four, string-based challenges in chapter five, game-based challenges in chapter six, count-based challenges in chapter seven, and miscellaneous challenges in chapter eight. Each chapter comprises a set of challenges with examples, hints, algorithms, and Python code solutions. The target audience of the book includes computer science and engineering students, teachers, software developers, and participants in programming competitions.
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Math Toolkit for Real-Time Programming
Do big math on small machines Write fast and accurate library functions Master analytical and numerical calculus Perform numerical integration to any order Implement z-transform formulas Need to learn the ins and outs of the fundamental math functions in
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Data Science Fundamentals for Python and MongoDB
Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn’t required because complete examples are provided and explained.Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to success is “rocky” at best. The coding examples in this book are concise, accurate, and complete, and perfectly complement the data science concepts introduced. What You'll LearnPrepare for a career in data scienceWork with complex data structures in PythonSimulate with Monte Carlo and Stochastic algorithmsApply linear algebra using vectors and matricesUtilize complex algorithms such as gradient descent and principal component analysisWrangle, cleanse, visualize, and problem solve with dataUse MongoDB and JSON to work with dataWho This Book Is ForThe novice yearning to break into the data science world, and the enthusiast looking to enrich, deepen, and develop data science skills through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming will make learning easier.
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Python for Engineers and Scientists
The text focuses on the basics of Python programming fundamentals and introduction to present-day applications in technology and the upcoming state-of-art trends in a comprehensive manner. The text is based on Python 3.x and it covers the fundamentals of Python with object-oriented concepts having numerous worked-out examples. It provides a learning tool for the students of beginner level as well as for researchers of advanced level. Each chapter contains additional examples that explain the usage of methods/functions discussed in the chapter. It provides numerous programming examples along with their outputs.The book:Includes programming tips to highlight the important concepts and help readers avoid common programming errorsProvides programming examples along with their outputs to ensure the correctness and help readers in mastering the art of writing efficient Python programsContains MCQs with their answers; conceptual questions and programming questions; and solutions to some selected programming questions, for every chapterDiscusses applications like time zone converter and password generators at the endCovers fundamental of Python up to object oriented concepts including regular expressionThe book offers a simple and lucid treatment of concepts supported with illustrations for easy understanding, provides numerous programming examples along with their outputs, and includes programming tips to highlight the important concepts. It will be a valuable resource for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
R 2,826.71
R 860.21
R 272.00
R 1,088.42
R 1,985.00
Introduction to Python Programming and Data Structures, Global Edition
For introductory courses in Python Programming and Data Structures. A fundamentals first approach to programming helps students create efficient, elegant code. Introduction to Python Programming and Data Structures introduces students to basic programming concepts using a fundamentals-first approach that prepares students to learn object-oriented programming and advanced Python programming. This approach presents programming concepts and techniques that include control statements, loops, functions, and arrays before designing custom classes. Students learn basic logic and programming concepts prior to moving into object-oriented and GUI programming. The content incorporates a wide variety of problems with various levels of difficulty and covers many application areas to engage and motivate students.
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Data Clustering with Python
Data clustering, an interdisciplinary field with diverse applications, has gained increasing popularity since its origins in the 1950s. Over the past six decades, researchers from various fields have proposed numerous clustering algorithms. In 2011, I wrote a book on implementing clustering algorithms in C++ using object-oriented programming. While C++ offers efficiency, its steep learning curve makes it less ideal for rapid prototyping. Since then, Python has surged in popularity, becoming the most widely used programming language since 2022. Its simplicity and extensive scientific libraries make it an excellent choice for implementing clustering algorithms.Features:Introduction to Python programming fundamentalsOverview of key concepts in data clusteringImplementation of popular clustering algorithms in PythonPractical examples of applying clustering algorithms to datasetsAccess to associated Python code on GitHubThis book extends my previous work by implementing clustering algorithms in Python. Unlike the object-oriented approach in C++, this book uses a procedural programming style, as Python allows many clustering algorithms to be implemented concisely. The book is divided into two parts: the first introduces Python and key libraries like NumPy, Pandas, and Matplotlib, while the second covers clustering algorithms, including hierarchical and partitional methods. Each chapter includes theoretical explanations, Python implementations, and practical examples, with comparisons to scikit-learn where applicable.This book is ideal for anyone interested in clustering algorithms, with no prior Python experience required. The complete source code is available at: https://github.com/ganml/dcpython.
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