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
  • 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
  • Hands-On Machine Learning With Scikit-Learn, Keras, And Tensorflow: Concepts, Tools, And Techniques To Build Intelligent Systems
ISBN: 1492032646
EAN13: 9781492032649
Language: English
Release Date: Oct 22, 2019
Pages: 600
Dimensions: 1" H x 9.19" L x 7" W
Weight: 1.11 lbs.
Format: Paperback
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Hands-On Machine Learning With Scikit-Learn, Keras, And Tensorflow: Concepts, Tools, And Techniques To Build Intelligent Systems

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Book Overview

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 Aur lien G ron 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 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

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Book Reviews (8)

4
  |   8  reviews
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1
   very poorly bound
It was very poorly bound. The jagged edges of the top edges of several pages are not separated. The book is ragged in the period of several tens of pages as in the second picture because the binding is irregular.
 
3
   Good content... I wasn't lucky with the delivery
The book is one of the best in Machine Learning. I suppose that my order was damaged on their way. It only has a bag with little bubbles, so I would like better packing. Delivery takes some time to this part of the world, so I don't want to return it.
 
5
   great book and nice ebook experience
This is an update on my previous review. I gave only one star for the ebook experience, but after reading the author's comments, I realized the publisher updated the ebook and everything is great. The book gives you a hands-on experience with machine learning. Most of the recent improvements in the field are covered.
 
5
   Excellent coverage in simple English
I can tell you that this book should not be required reading in this subject, as I am finishing up an MCS in Data Science fromUIUC. They assumed too much in the required ML course at school. In this book, it was easy to understand the topics in a way that makes sense. Images, graphs, and tables are clear and help a lot by showing the text explanation. I didn't notice anything critical. It's not a light read, but it's easy to model-by-model, as it comes in at almost 800 pages.
 
5
   Look no Further!
Aurelien did the same thing. Whether you are a data scientist looking to start building models in Python or a software developer looking to become an engineer, look no further! The balance between theory background and implementation that was present in the first edition is retained. The Jupyter notes are more than helpful. The illustrations in the printed version are now in color, which makes it easier to read. This book is a must-have for a data scientist with Python.
 
5
   Best Machine Learning book I own
I'm very happy with the book. I enjoy the little bits of humor, and it does a great job not glossing over important details that may be a stumbling block for someone. He went into depth on setting up virtual environments and best practices, which I appreciated. Having this explained so well is going to save someone a lot of time, I remember years back when I was starting that concept, I tripped up so much. His code is very far away to be written in a thoughtful way and has all of it on the internet. He goes into a lot of gotchas and tips that seem to add a certain maturity to his writing. He is very knowledgeable in machine learning. I would recommend. It's been more interesting than I expected.
 
5
   Best in class book
I've read a lot of machine learning related books and this one is the best. I was looking forward to the second edition of this book. It is updated for TensorFlow 2. It's helpful to highlight key points in the edition. Once the print edition is released, I will receive it as well. The print edition of the book is now in color. The first edition wasn't. It makes it easier to read with various charts and graphics.
 
3
   Math fonts in kindle edition
I enjoy learning from this book, but the math in the kindle edition is a mess which makes the reading unpleasant. I know it shouldn't be a deal breaker but for someone who wants to move from hard copy to kindle it was disappointing.
 
1