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Hands-On Machine Learning Summary

Hands-On Machine Learning with Scikit-Learn: 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.

About the Author

Aurélien Géron is a Machine Learning consultant. A former Googler, he led the YouTube video classification team from 2013 to 2016. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car-sharing service Autolib’.

Before this, he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada’s DOD), and healthcare (blood transfusion). He published a few technical books (on C++, WiFi, and Internet architectures), and was a Computer Science lecturer in a French engineering school.

A few fun facts: he taught his 3 children to count in binary with their fingers (up to 1023), he studied microbiology and evolutionary genetics before going into software engineering, and his parachute didn’t open on the 2nd jump.

Hands-On Machine Learning Introduction

Excerpt. © Reprinted by permission. All rights reserved.

The Machine Learning Tsunami

In 2006, Geoffrey Hinton et al. published a paper1 showing how to train a deep neural network capable of recognizing handwritten digits with state-of-the-art precision (>98%). They branded this technique “Deep Learning.” A deep neural network is a (very) simplified model of our cerebral cortex, composed of a stack of layers of artificial neurons. Training a deep neural net was widely considered impossible at the time,2 and most researchers had abandoned the idea in the late 1990s.

This paper revived the interest of the scientific community, and before long many new papers demonstrated that Deep Learning was not only possible but capable of mind-blowing achievements that no other Machine Learning (ML) technique could hope to match (with the help of tremendous computing power and great amounts of data). This enthusiasm soon extended to many other areas of Machine Learning.

A decade or so later, Machine Learning has conquered the industry: it is at the heart of much of the magic in today’s high-tech products, ranking your web search results, powering your smartphone’s speech recognition, recommending videos, and beating the world champion at the game of Go. Before you know it, it will be driving your car.

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 learn 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 more than likely you could unearth some hidden gems if you just knew where to look. With Machine Learning, you could accomplish the following and more:

• 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.

Whatever the reason, you have decided to learn Machine Learning and implement it in your projects. Great idea!

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 learning Machine Learning.

TensorFlow is a more complex library for distributed numerical computation. It makes it possible to train and run very large neural networks efficiently by distributing the computations across potentially hundreds of multi-GPU (graphics processing unit) servers. TensorFlow (TF) was created at Google and supports many of its large-scale Machine Learning applications. It was open-sourced in November 2015.

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).

The book favors a hands-on approach, growing an intuitive understanding of Machine Learning through concrete working examples and just a little bit of theory. While you can read this book without picking up your laptop, I highly recommend you experiment with the code examples available online as Jupyter notebooks at https://github.com/ageron/handson-ml2.

Prerequisites

This book assumes that you have some Python programming experience and that you are familiar with Python’s main scientific libraries—in particular, NumPy, pandas, and Matplotlib.

Also, if you care about what’s under the hood, you should have a reasonable understanding of college-level math as well (calculus, linear algebra, probabilities, and statistics).

If you don’t know Python yet, http://learnpython.org/ is a great place to start. The official tutorial on Python.org is also quite good.

If you have never used Jupyter, Chapter 2 will guide you through installation and the basics: it is a powerful tool to have in your toolbox.

If you are not familiar with Python’s scientific libraries, the provided Jupyter notebooks include a few tutorials. There is also a quick math tutorial for linear algebra.

Roadmap

This book is organized in two parts. Part I, The Fundamentals of Machine Learning, covers the following topics:

• What Machine Learning is, what problems it tries to solve, and the main categories and fundamental concepts of its systems
• The steps in a typical Machine Learning project
• Learning by fitting a model to data
• Optimizing a cost function
• Handling, cleaning, and preparing data
• Selecting and engineering features
• Selecting a model and tuning hyperparameters using cross-validation
• The challenges of Machine Learning, in particular underfitting and overfitting (the bias/variance trade-off)
• The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods
• Reducing the dimensionality of the training data to fight the “curse of dimensionality”
• Other unsupervised learning techniques, including clustering, density estimation, and anomaly detection

Part II, Neural Networks and Deep Learning, covers the following topics:

• What neural nets are and what they’re good for
• Building and training neural nets using TensorFlow and Keras
• The most important neural net architectures: feedforward neural nets for tabular data, convolutional nets for computer vision, recurrent nets and long short-term memory (LSTM) nets for sequence processing, encoder/decoders and Transformers for natural language processing, autoencoders and generative adversarial networks (GANs) for generative learning
• Techniques for training deep neural nets
• How to build an agent (e.g., a bot in a game) that can learn good strategies through trial and error, using Reinforcement Learning
• Loading and preprocessing large amounts of data efficiently
• Training and deploying TensorFlow models at scale

The first part is based mostly on Scikit-Learn, while the second part uses TensorFlow and Keras.

Changes in the Second Edition

This second edition has six main objectives:

1. Cover additional ML topics: more unsupervised learning techniques (including clustering, anomaly detection, density estimation, and mixture models); more techniques for training deep nets (including self-normalized networks); additional computer vision techniques (including Xception, SENet, object detection with YOLO, and semantic segmentation using R-CNN); handling sequences using covolutional neural networks (CNNs, including WaveNet); natural language processing using recurrent neural networks (RNNs), CNNs, and Transformers; and GANs.
2. Cover additional libraries and APIs (Keras, the Data API, TF-Agents for Reinforcement Learning) and training and deploying TF models at scale using the Distribution Strategies API, TF-Serving, and Google Cloud AI Platform. Also briefly introduce TF Transform, TFLite, TF Addons/Seq2Seq, and TensorFlow.js.
3. Discuss some of the latest important results from Deep Learning research.
4. Migrate all TensorFlow chapters to TensorFlow 2, and use TensorFlow’s implementation of the Keras API (tf.keras) whenever possible.
5. Update the code examples to use the latest versions of Scikit-Learn, NumPy, pandas, Matplotlib, and other libraries.
6. Clarify some sections and fix some errors, thanks to plenty of great feedback from readers.

Some chapters were added, others were rewritten, and a few were reordered. See https://homl.info/changes2 for more details on what changed in the second edition.

Other Resources

Many excellent resources are available to learn about Machine Learning. For example, Andrew Ng’s ML course on Coursera is amazing, although it requires a significant time investment (think months).

There are also many interesting websites about Machine Learning, including of course Scikit-Learn’s exceptional User Guide. You may also enjoy Dataquest, which provides very nice interactive tutorials, and ML blogs such as those listed on Quora. Finally, the Deep Learning website has a good list of resources to check out to learn more.

There are many other introductory books about Machine Learning. In particular:

• Joel Grus’s Data Science from Scratch (O’Reilly) presents the fundamentals of Machine Learning and implements some of the main algorithms in pure Python (from scratch, as the name suggests).
• Stephen Marsland’s Machine Learning: An Algorithmic Perspective (Chapman & Hall) is a great introduction to Machine Learning, covering a wide range of topics in depth with code examples in Python (also from scratch, but using NumPy).
• Sebastian Raschka’s Python Machine Learning (Packt Publishing) is also a great introduction to Machine Learning and leverages Python open source libraries (Pylearn 2 and Theano).
• François Chollet’s Deep Learning with Python (Manning) is a very practical book that covers a large range of topics in a clear and concise way, as you might expect from the author of the excellent Keras library. It favors code examples over mathematical theory.
• Andriy Burkov’s The Hundred-Page Machine Learning Book is very short and covers an impressive range of topics, introducing them in approachable terms without shying away from the math equations.
• Yaser S. Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin’s Learning from Data (AMLBook) is a rather theoretical approach to ML that provides deep insights, in particular on the bias/variance trade-off (see Chapter 4).
• Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach, 3rd Edition (Pearson), is a great (and huge) book covering an incredible amount of topics, including Machine Learning. It helps put ML into perspective.

Finally, joining ML competition websites such as Kaggle.com will allow you to practice your skills on real-world problems, with help and insights from some of the best ML professionals out there.

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Product details:

Edition	International Edition
ISBN	1492032646, 978-1492032649
Posted on	October 15, 2019
Format	pdf
Page Count	856 pages
Author	Aurélien Géron