Top learning python

Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutzs popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. Its an ideal way to begin, whether youre new to programming or a professional developer versed in other languages.

Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3 the latest releases in the 3.X and 2.X linesplus all other releases in common use today. Youll also learn some advanced language features that recently have become more common in Python code.

• Explore Pythons major built-in object types such as numbers, lists, and dictionaries
• Create and process objects with Python statements, and learn Pythons general syntax model
• Use functions to avoid code redundancy and package code for reuse
• Organize statements, functions, and other tools into larger components with modules
• Dive into classes: Pythons object-oriented programming tool for structuring code
• Write large programs with Pythons exception-handling model and development tools
• Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.

By using concrete examples, minimal theory, and two production-ready Python frameworksscikit-learn and TensorFlowauthor Aurlien Gron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what youve learned, all you need is programming experience to get started.

• Explore the machine learning landscape, particularly neural nets
• Use scikit-learn to track an example machine-learning project end-to-end
• Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
• Use the TensorFlow library to build and train neural nets
• Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
• Learn techniques for training and scaling deep neural nets
• Apply practical code examples without acquiring excessive machine learning theory or algorithm details

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 Franois 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 learninga 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 Franois 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

Franois 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

1. What is deep learning?
2. Before we begin: the mathematical building blocks of neural networks
3. Getting started with neural networks
4. Fundamentals of machine learning

PART 2 - DEEP LEARNING IN PRACTICE

5. Deep learning for computer vision
6. Deep learning for text and sequences
7. Advanced deep-learning best practices
8. Generative deep learning
9. Conclusions
10. appendix A - Installing Keras and its dependencies on Ubuntu
11. appendix B - Running Jupyter notebooks on an EC2 GPU instance

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

Youll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Mller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, youll learn:

• Fundamental concepts and applications of machine learning
• Advantages and shortcomings of widely used machine learning algorithms
• How to represent data processed by machine learning, including which data aspects to focus on
• Advanced methods for model evaluation and parameter tuning
• The concept of pipelines for chaining models and encapsulating your workflow
• Methods for working with text data, including text-specific processing techniques
• Suggestions for improving your machine learning and data science skills

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If youre comfortable with Python and its libraries, including pandas and scikit-learn, youll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics.

Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.

Youll find recipes for:

• Vectors, matrices, and arrays
• Handling numerical and categorical data, text, images, and dates and times
• Dimensionality reduction using feature extraction or feature selection
• Model evaluation and selection
• Linear and logical regression, trees and forests, and k-nearest neighbors
• Support vector machines (SVM), nave Bayes, clustering, and neural networks
• Saving and loading trained models