Hands On Machine Learning with Scikit Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

This Practical Book Shows You How. By Using Concrete Examples, Minimal Theory, And Two Production-Ready Python Frameworks―Scikit-Learn And Tensorflow―Author Aurélien Géron Helps You Gain An Intuitive Understanding Of The Concepts And Tools For Building Intelligent Systems

Hands On Machine Learning with Scikit Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Aurélien Géron

O'Reilly Media

2019

Abstract

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.

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 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

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.

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).

Contents:

• The fundamentals of machine learning: the machine learning landscape, and-to-end machine learning project, classification, training models, support vector machines, decion trees, ensemble learning and random forests, dimensionality reduction, unsupervised learning techiniques;
• Neural networks and deep learning: introduction to Artificial Neural Networks with Keras, training deep neural networks, custom models and training with tensorflow, loading and preprocessing data with tensorflow, deep computer vision using convolutional neural networks, processing with RNNs and attention, representation learning and generative learning using autoencoders and GANs, Reinforcement learning, training and deploying tensorflow models at scale.

Citation

Aurélien Géron; Hands On Machine Learning with Scikit Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems ; O'Reilly Media; 2019

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