Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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 TensorFlow?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 netsUse Scikit-Learn to track an example machine-learning project end-to-endExplore several training models, including support vector machines, decision trees, random forests, and ensemble methodsUse the TensorFlow library to build and train neural netsDive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learningLearn techniques for training and scaling deep neural nets
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Description
Editorial Reviews
About the Author
A machine learning consultant and trainer is Aurélien Géron. He was the leader of the video classification team at YouTube. From 2002 to 2012 he was a founder and CTO of a leading Wireless ISP in France, as well as a founder and CTO of two consulting firms.
–This text refers to the paperback edition.
From the Publisher
Prerequisites
The book assumes that you have some Python programming experience and that you are familiar with Python?s main scientific libraries.
You should have a reasonable understanding of college-level math if you care about what?s under the hood.
More about this book
Machine Learning in Your Projects
You are excited about Machine Learning and would love to join the party. Wouldn’t it be great if you could give your homemade robot a brain of its own? Is it possible to make it recognize faces? Do you want it to walk around? If you know where to look, you could find some hidden gems in your company’s data. You could do the following with Machine Learning.
- Find the best marketing strategy for each group.
- Recommendations for products are based on what similar clients bought.
- It is possible to detect which transactions are likely to be fraudulent.
- Next year?s revenue is forecast.
- 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.
We will be using production-ready Python frameworks instead of implementing our own toy versions.
- It makes for a great entry point to learn Machine Learning because it is very easy to use.
- The library is used for distributed numerical computation. It is possible to train and run large neural networks efficiently by distributing the computations across hundreds of multi-GPU server. Many large-scale applications are supported by TensorFlow. Since Nov. 2015, it’s been open source.
- 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).
Additional information
| Best Sellers Rank | #24,961 in Kindle Store (See Top 100 in Kindle Store) #1 in Pattern Recognition #1 in Neural Networks #2 in AI & Semantics |
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