Search results for "Python in Depth"
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Python Without Fear
Praise for this book, Python Without Fear “This is really a great book. I wish I’d had it when I was learning Python.” –John M. Wargo, author of Apache Cordova 4 Programming Praise for the previous book in the series, C++ Without Fear “I’m in love with your C++ Without Fear book. It keeps me awake for hours during the night. Thanks to you, I got most of the idea in just a few hours.” –Laura Viral, graduate physics student at CERN and Istanbul, Turkey “It’s hard to tell where I began and ended with your book. I felt like I woke up and literally knew how to write C++ code. I can’t overstate the confidence you gave me.” – Danny Grady, senior programmer/analyst at a Fortune 500 Company Whether you’re new to programming or moving from another language, Python Without Fear will quickly make you productive! Brian Overland’s unique approach to Python includes: Taking you by the hand while teaching topics from the very basics to intermediate and advanced features of PythonTeaching by examples that are explained line by lineHeavy emphasis on examples that are fun and useful, including games, graphics, database applications, file storage, puzzles, and more!How to think “Pythonically” and avoid common “gotchas” Register your product at informit.com/register for convenient access to downloads, updates, and/or corrections as they become available.
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Introduction to Python and Large Language Models
Gain a solid foundation for Natural Language Processing (NLP) and Large Language Models (LLMs), emphasizing their significance in today’s computational world. This book is an introductory guide to NLP and LLMs with Python programming.The book starts with the basics of NLP and LLMs. It covers essential NLP concepts, such as text preprocessing, feature engineering, and sentiment analysis using Python. The book offers insights into Python programming, covering syntax, data types, conditionals, loops, functions, and object-oriented programming. Next, it delves deeper into LLMs, unraveling their complex components.You’ll learn about LLM elements, including embedding layers, feedforward layers, recurrent layers, and attention mechanisms. You’ll also explore important topics like tokens, token distributions, zero-shot learning, LLM hallucinations, and insights into popular LLM architectures such as GPT-4, BERT, T5, PALM, and others. Additionally, it covers Python libraries like Hugging Face, OpenAI API, and Cohere. The final chapter bridges theory with practical application, offering step-by-step examples of coded applications for tasks like text generation, summarization, language translation, question-answering systems, and chatbots.In the end, this book will equip you with the knowledge and tools to navigate the dynamic landscape of NLP and LLMs.What You’ll LearnUnderstand the basics of Python and the features of Python 3.11Explore the essentials of NLP and how do they lay the foundations for LLMs.Review LLM components.Develop basic apps using LLMs and Python.Who This Book Is ForData analysts, AI and Machine Learning Experts, Python developers, and Software Development Professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks.
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Python: Deeper Insights into Machine Learning
Leverage benefits of machine learning techniques using PythonAbout This Book* Improve and optimise machine learning systems using effective strategies.* Develop a strategy to deal with a large amount of data.* Use of Python code for implementing a range of machine learning algorithms and techniques.Who This Book Is ForThis title is for data scientist and researchers who are already into the field of data science and want to see machine learning in action and explore its real-world application. Prior knowledge of Python programming and mathematics is must with basic knowledge of machine learning concepts.What You Will Learn* Learn to write clean and elegant Python code that will optimize the strength of your algorithms* Uncover hidden patterns and structures in data with clustering* Improve accuracy and consistency of results using powerful feature engineering techniques* Gain practical and theoretical understanding of cutting-edge deep learning algorithms* Solve unique tasks by building models* Get grips on the machine learning design processIn DetailMachine learning and predictive analytics are becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. It is one of the fastest growing trends in modern computing, and everyone wants to get into the field of machine learning. In order to obtain sufficient recognition in this field, one must be able to understand and design a machine learning system that serves the needs of a project.The idea is to prepare a learning path that will help you to tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques. Also, it will give you a solid foundation in the machine learning design process, and enable you to build customized machine learning models to solve unique problems.The course begins with getting your Python fundamentals nailed down. It focuses on answering the right questions that cove a wide range of powerful Python libraries, including scikit-learn Theano and Keras.After getting familiar with Python core concepts, it's time to dive into the field of data science. You will further gain a solid foundation on the machine learning design and also learn to customize models for solving problems.At a later stage, you will get a grip on more advanced techniques and acquire a broad set of powerful skills in the area of feature selection and feature engineering.Style and approachThis course includes all the resources that will help you jump into the data science field with Python. The aim is to walk through the elements of Python covering powerful machine learning libraries. This course will explain important machine learning models in a step-by-step manner. Each topic is well explained with real-world applications with detailed guidance.Through this comprehensive guide, you will be able to explore machine learning techniques.
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Offensive Security Using Python
Unlock Python's hacking potential and discover the art of exploiting vulnerabilities in the world of offensive cybersecurityKey FeaturesGet in-depth knowledge of Python's role in offensive security, from fundamentals through to advanced techniquesDiscover the realm of cybersecurity with Python and exploit vulnerabilities effectivelyAutomate complex security tasks with Python, using third-party tools and custom solutionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionOffensive Security Using Python is your go-to manual for mastering the quick-paced field of offensive security. This book is packed with valuable insights, real-world examples, and hands-on activities to help you leverage Python to navigate the complicated world of web security, exploit vulnerabilities, and automate challenging security tasks.From detecting vulnerabilities to exploiting them with cutting-edge Python techniques, you’ll gain practical insights into web security, along with guidance on how to use automation to improve the accuracy and effectiveness of your security activities. You’ll also learn how to design personalized security automation tools. While offensive security is a great way to stay ahead of emerging threats, defensive security plays an equal role in protecting organizations from cyberattacks. In this book, you’ll get to grips with Python secure coding techniques to improve your ability to recognize dangers quickly and take appropriate action. As you progress, you’ll be well on your way to handling the contemporary challenges in the field of cybersecurity using Python, as well as protecting your digital environment from growing attacks.By the end of this book, you’ll have a solid understanding of sophisticated offensive security methods and be able to stay ahead in the constantly evolving cybersecurity space.What you will learnFamiliarize yourself with advanced Python techniques tailored to security professionals' needsUnderstand how to exploit web vulnerabilities using PythonEnhance cloud infrastructure security by utilizing Python to fortify infrastructure as code (IaC) practicesBuild automated security pipelines using Python and third-party toolsDevelop custom security automation tools to streamline your workflowImplement secure coding practices with Python to boost your applicationsDiscover Python-based threat detection and incident response techniquesWho this book is forThis book is for a diverse audience interested in cybersecurity and offensive security. Whether you're an experienced Python developer looking to enhance offensive security skills, an ethical hacker, a penetration tester eager to learn advanced Python techniques, or a cybersecurity enthusiast exploring Python's potential in vulnerability analysis, you'll find valuable insights. If you have a solid foundation in Python programming language and are eager to understand cybersecurity intricacies, this book will help you get started on the right foot.
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Thinking in Pandas
Understand and implement big data analysis solutions in pandas with an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by exploring its underlying implementation and data structures. Thinking in Pandas introduces the topic of big data and demonstrates concepts by looking at exciting and impactful projects that pandas helped to solve. From there, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Author Hannah Stepanek explains how to load and normalize data in pandas efficiently, and reviews some of the most commonly used loaders and several of their most powerful options. You will then learn how to access and transform data efficiently, what methods to avoid, and when to employ more advanced performance techniques. You will also go over basic data access and munging in pandas and the intuitive dictionary syntax. Choosing the right DataFrame format, working with multi-level DataFrames, and how pandas might be improved upon in the future are also covered.By the end of the book, you will have a solid understanding of how the pandas library works under the hood. Get ready to make confident decisions in your own projects by utilizing pandas—the right way.What You Will LearnUnderstand the underlying data structure of pandas and why it performs the way it does under certain circumstancesDiscover how to use pandas to extract, transform, and load data correctly with an emphasis on performanceChoose the right DataFrame so that the data analysis is simple and efficient.Improve performance of pandas operations with other Python libraries Who This Book Is ForSoftware engineers with basic programming skills in Python keen on using pandas for a big data analysis project. Python software developers interested in big data.
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Hands-On Natural Language Processing with Python
Foster your NLP applications with the help of deep learning, NLTK, and TensorFlowKey FeaturesWeave neural networks into linguistic applications across various platformsPerform NLP tasks and train its models using NLTK and TensorFlowBoost your NLP models with strong deep learning architectures such as CNNs and RNNsBook DescriptionNatural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges.To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow.By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.What you will learnImplement semantic embedding of words to classify and find entitiesConvert words to vectors by training in order to perform arithmetic operationsTrain a deep learning model to detect classification of tweets and newsImplement a question-answer model with search and RNN modelsTrain models for various text classification datasets using CNNImplement WaveNet a deep generative model for producing a natural-sounding voiceConvert voice-to-text and text-to-voiceTrain a model to convert speech-to-text using DeepSpeechWho this book is forHands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.
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Learn Python Programming
Learn the fundamentals of Python (3.7) and how to apply it to data science, programming, and web development. Fully updated to include hands-on tutorials and projects.Key FeaturesLearn the fundamentals of Python programming with interactive projectsApply Python to data science with tools such as IPython and JupyterUtilize Python for web development and build a real-world app using DjangoBook DescriptionLearn Python Programming is a quick, thorough, and practical introduction to Python - an extremely flexible and powerful programming language that can be applied to many disciplines. Unlike other books, it doesn't bore you with elaborate explanations of the basics but gets you up-and-running, using the language. You will begin by learning the fundamentals of Python so that you have a rock-solid foundation to build upon. You will explore the foundations of Python programming and learn how Python can be manipulated to achieve results. Explore different programming paradigms and find the best approach to a situation; understand how to carry out performance optimization and effective debugging; control the flow of a program; and utilize an interchange format to exchange data. You'll also walk through cryptographic services in Python and understand secure tokens. Learn Python Programming will give you a thorough understanding of the Python language. You'll learn how to write programs, build websites, and work with data by harnessing Python's renowned data science libraries. Filled with real-world examples and projects, the book covers various types of applications, and concludes by building real-world projects based on the concepts you have learned.What you will learnGet Python up and running on Windows, Mac, and LinuxExplore fundamental concepts of coding using data structures and control flowWrite elegant, reusable, and efficient code in any situationUnderstand when to use the functional or OOP approachCover the basics of security and concurrent/asynchronous programmingCreate bulletproof, reliable software by writing testsBuild a simple website in DjangoFetch, clean, and manipulate dataWho this book is forLearn Python Programming is for individuals with relatively little experience in coding or Python. It's also ideal for aspiring programmers who need to write scripts or programs to accomplish tasks. The book shows you how to create a full-fledged application.
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Numerical Methods in Physics with Python
Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.
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Python
This book is an introduction to programming concepts that uses Python 3 as the target language. It follows a practical just-in-time presentation - material is given to the student when it is needed. Many examples will be based on games, because Python has become the language of choice for basic game development. Designed as a Year One textbook for introduction to programming classes or for the hobbyist who wants to learn the fundamentals of programming, the text assumes no programming experience. Features:Introduces programming concepts that use Python 3Includes many examples based on video game development4-color throughout with game demos on the companion files
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Intuitive Python
Developers power their projects with Python because it emphasizes readability, ease of use, and access to a meticulously maintained set of packages and tools. The language itself continues to improve with every release: writing in Python is full of possibility. But to maintain a successful Python project, you need to know more than just the language. You need tooling and instincts to help you make the most out of what's available to you. Use this book as your guide to help you hone your skills and sculpt a Python project that can stand the test of time.No matter your experience level or background, Python's batteries-included standard library and rich third-party ecosystem provide a solid foundation to build your projects on. With the right intuition and background knowledge, you can take advantage of all the power Python offers. Take a guided tour of some of Python's high points to craft a project that you can sustain and build on for a long time.Run static analysis tools to detect and eliminate classes of bugs before you run code. Experiment with Python's concurrency model and develop patterns for using Python's thread and process abstractions to their full potential. Introduce yourself to Python's type hinting system: mypy. Download and run third-party Python packages and do so safely without compromising on security. Debug code using Python's built in debugger, and try procedures out in the interactive console. Run your code under new versions of the Python interpreter to unlock performance and usability improvements. All along the way, sharpen your Python instincts so you can keep your code clean and reduce the chance of bugs.Mine Python for all you can by playing to its strengths and embracing patterns that harness its potential.What You Need:The books assumes you have some experience programming in any language (not necessarily Python). To run the code presented in the book, you'll need a Python environment which you can download from https://www.python.org/downloads/.
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Mastering Machine Learning with Python in Six Steps
Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.What You'll LearnUnderstand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNNWho This Book Is ForPython developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
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Head First Python
What will you learn from this book? Want to learn the Python language without slogging your way through how-to manuals? With Head First Python, you'll quickly grasp Python's fundamentals by working with built-in data structures and functions. You'll build your very own web app, which--once it's ready for prime time--runs in the cloud. You'll learn how to wrangle data with Python, scrape data from the web, feed data to pandas, and interact with databases. This third edition is a complete learning experience that will help you become a bona fide Python programmer in no time. What's so special about this book? If you've read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. With this book, you'll learn Python through a multisensory experience that engages your mind--rather than a text-heavy approach that puts you to sleep.
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Silicon Valley Python Engineer Interview Guide
Silicon Valley Python Interview Guide: Data Structures, Algorithms, and System Design is an essential resource for aspiring software engineers preparing for technical interviews at top-tier companies. This book provides a comprehensive roadmap, covering foundational concepts, practical coding techniques, and advanced problem-solving strategies to help candidates excel in interviews. With a focus on Python, the book equips readers with the skills to tackle challenging coding problems, design scalable systems, and communicate solutions effectively. In the first half, the book delves into core data structures (lists, stacks, queues, graphs, and trees) and algorithms (binary search, dynamic programming, DFS, BFS, and backtracking),offering practical examples and Python implementations. The latter half transitions to system design, including big data architectures, distributed systems, and machine learning workflows. Case studies on real-world applications like Tiny URL, autocomplete systems, and Chat GPT-like models provide hands-on insights. Whether you are an early-career engineer or an experienced professional, this guide is designed to enhance your preparation with real-world examples, tested code, and proven strategies. It is more than a technical handbook—it is your roadmap to building confidence and securing a role in the competitive tech industry.
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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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