Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition by Aurélien Géron is a PDF book for free download.
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 frameworks—Scikit-Learn and Tensor Flow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll 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 you’ve 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 Tensor Flow 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.
More about this book:
Machine Learning in Your Projects
So, naturally you are excited about Machine Learning and would love to join the party! Perhaps you would like to give your homemade robot a brain of its own? Make it recognize faces? Or teach it to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and you could likely unearth some hidden gems if you just knew where to look. With Machine Learning, you could accomplish the following:
Segment customers and find the best marketing strategy for each group
Recommend products for each client based on what similar clients bought
Detect which transactions are likely to be fraudulent
Forecast next year’s revenue
And more
Objective and Approach:
This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, tools, and intuition you need to implement programs capable of learning from data. We will cover a large number of techniques, from the simplest and most commonly used (such as linear regression) to some of the Deep Learning techniques that regularly win competitions.
Rather than implementing our own toy versions of each algorithm, we will be using production-ready Python frameworks:
Scikit-Learn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learn Machine Learning.
TensorFlow is a more complex library for distributed numerical computation. It makes it possible to train & run very large neural networks efficiently by distributing the computations across potentially hundreds of multi-GPU servers. TensorFlow was created at Google and supports many of its large-scale applications. It's been open source since Nov. 2015, with version 2.0 releasing Oct 2019.
Keras is a high-level Deep Learning API that makes it very simple to train and run neural networks. It can run on top of either TensorFlow, Theano, or Microsoft Cognitive Toolkit (formerly known as CNTK). TensorFlow comes with its own implementation of this API, called tf.keras, which provides support for some advanced TensorFlow features (e.g., the ability to efficiently load data).
About the Author:
Aurélien Géron is a machine learning consultant and trainer. A former Googler, he led YouTube's video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst (a leading Wireless ISP in France) from 2002 to 2012, and a founder and CTO of two consulting firms -- Polyconseil (telecom, media and strategy) and Kiwisoft (machine learning and data privacy).
Contents:
Part I. The Fundamentals of Machine Learning
1. The Machine Learning Landscape
2. End-to-End Machine Learning Project
3. Classi€cation
4. Training Models
5. Support Vector Machines
6. Decision Trees
7. Ensemble Learning and Random Forests
8. Dimensionality Reduction
9. Unsupervised Learning Techniques
Part II. Neural Networks and Deep Learning
10. Introduction to Arti€cial Neural Networks with Keras
11. Training Deep Neural Networks
12. Custom Models and Training with TensorFlow
13. Loading and Preprocessing Data with TensorFlow
14. Deep Computer Vision Using Convolutional Neural Networks
About the book:
Publisher : O'Reilly Media; 2nd edition (October 15, 2019)
Language : English
Paperback : 510 pages
Item Weight : 2.8 pounds
Dimensions : 7 x 1.2 x 9.2 inches
File: PDF, 12Mb
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