The book starts the Python language from the basics and then intermediate and advanced topics are covered. After functional programming is explained in detail, object-oriented programming features such as classes, inheritance, abstract classes, polymorphism are described. Data structures and collections are given for both fundamental and advanced usage. The book contains new and advanced features such as magic functions, type checking.
The book covers data science topics in Python language. Libraries such as Numpy, Matplotlib, Pandas, Scipy are explained in detail. In addition to data science, the book contains the usage of many libraries for developers of Python. The basic knowledge needed to use Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision features are covered. The book contains tools for data analysis and business intelligence.
The book contains Python libraries used in many applications. Internet, Downloads, JSON, REST are covered. Utilities such as time, random, regular expressions are included. The operating systems & process are explained in detail. File system operations and Pathlib are covered. Some introductions to Big Data & Artificial Intelligence are added. CSV, Samples are explained as a preperation for data science. Visual libraries such as PIL & Matplotlib are included. Speech Recognition is covered. Finally Tk is is explained & a full sample application is supplied.
Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. 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 Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François 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 deep-learning research, with a focus on computer vision and the application of machine learning to formal 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. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance
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Deep Learning With Python Illustrated Guide For Beginners And Intermediates "Learn By Doing Approach" Includes Keras with Tensorflow Backend Deep learning originates from a broader family of machine learning, including supervised and unsupervised learning The python programming language is one of the most popular languages for programmers in the 21st century. This programming language has been a fundamental cornerstone in a lot of technology we use today. -Things we take for granted on a daily basis. Developing both desktop and web applications, and more interestingly enough has been used to accomplish many artificial intelligence feats. The world is constantly changing and evolving and it appears machine learning could be the way of the future! As we speak technology on a massive scale is being developed to replace mundane and repetitive tasks humans interface with everyday through the use of "deep learning". Ultimately, this means less human errors and a more efficient ways of operating for many corporations. You can potentially become the next big start-up! Develop software, web development tools and many more online ventures! Companies That Use Python Currently Google Facebook Dropbox Yahoo IBM Mozilla Quora Why Programmers Choose To Use Python? Readable & Maintainable Code Dynamic Type System Compatible with Major Platforms and Systems Robust Standard Library Simplifies Complex Software Development Test Driven Development Highly Sought After Skill-Set For Employers Invest in your knowledge base by buying your copy right now. The greatest investment you can make is an investment in yourself! Python will pave the road of technological advancements and very much so shape the world we live in. Become apart of this global progression towards advanced technology through the use of "deep learning". What You'll Learn What is deep learning Theory of Artificial Neural Network Artificial Neural Network with Keras Image Classification with Convolutional Neural Network Environment Setup Natural Language Processing Evaluating and Tuning the ANN Sequence Modeling And, much, much more! By the end of this book you will have grasped the fundamentals of python programming & deep learning! There is also illustrations to go along to help you understand and retain the info on a much more profound level. Picture diagrams have scientifically proven to accelerate the learning process by over 120%! Buy Your Copy Right Now!
Python Mastery Unleashed: Advanced Programming Techniques is a comprehensive guide to mastering advanced programming techniques in Python. Designed for seasoned Python developers and aspiring programmers alike, this book offers a comprehensive understanding of the advanced programming techniques used by experienced Python developers to build complex systems and applications. The book begins with a review of basic programming principles and data structures in Python, including control structures, data types, and algorithms. It then progresses to advanced topics such as functional programming, object-oriented programming, and concurrency. Throughout the book, readers will learn how to use techniques such as decorators, generators, context managers, and metaclasses to build robust and maintainable Python applications. In addition, the book explores the best practices for testing, debugging, and profiling Python code, as well as strategies for managing code complexity and ensuring code quality. By the end of this book, readers will have a deep understanding of advanced Python programming techniques, and be equipped with the skills needed to build complex Python applications with ease. So if you're looking to take your Python programming skills to the next level, Python Mastery Unleashed: Advanced Programming techniques is the book for you.
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Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key FeaturesYour entry point into the world of artificial intelligence using the power of PythonAn example-rich guide to master various RL and DRL algorithmsExplore the power of modern Python libraries to gain confidence in building self-trained applicationsBook Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: Hands-On Reinforcement Learning with Python by Sudharsan RavichandiranPython Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa ShanmugamaniWhat you will learnTrain an agent to walk using OpenAI Gym and TensorFlowSolve multi-armed-bandit problems using various algorithmsBuild intelligent agents using the DRQN algorithm to play the Doom gameTeach your agent to play Connect4 using AlphaGo ZeroDefeat Atari arcade games using the value iteration methodDiscover how to deal with discrete and continuous action spaces in various environmentsWho this book is for If you’re an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.
