- Paperback: 576 pages
- Publisher: O'Reilly Media; 1 edition (April 9, 2017)
- Language: English
- ISBN-10: 1491962291
- ISBN-13: 978-1491962299
- Product Dimensions: 7 x 1.2 x 9.2 inches
- Shipping Weight: 2 pounds (View shipping rates and policies)
- Average Customer Review: 116 customer reviews
- Amazon Best Sellers Rank: #1,381 in Books (See Top 100 in Books)
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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 1st Edition
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From the Publisher
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).
More about this book
Machine Learning in Your Projects
Naturally you are excited about Machine Learning and you 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.
- 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
- Predict 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, the intuitions, and the tools you need to actually 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 actual 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 using data flow graphs. It makes it possible to train and run very large neural networks efficiently by distributing the computations across potentially thousands of multi-GPU servers. TensorFlow was created at Google and supports many of their large-scale Machine Learning applications. It was open-sourced in November 2015.
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.
Top customer reviews
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I purchased the kindle version so I can dive into this book early before the book comes out. I am not disappointed. It gives you the code on the familiar Python notebook to work on. The author really knows about Tensorflow and machine learning, and his teaching shows. There are pieces of information hard to find somewhere else, and I have spent hundreds to thousands to attend workshops.
Needless to say, I have not done all the exercises yet. But I like this book enough that I will work on all the problems I am interested in.
One disappointment though. I was hoping Keras, a high level api that enables fast experiments, is covered. It is not in this version. Sure hope it will be covered in the updated version.
I got this book for the deep learning portion (about half of the overall book length), and was shocked at the clarity of the conceptual explanations and code implementations. I've read many extensive explanations of important neural network architectures (FFNs, CNNs, RNNs, ...) and none of them were this clear and intuitive. Within 5 days I was able to go from having zero deep learning experience to easily implementing complicated architectures with TensorFlow.
Many people recommend Keras as an alternative to TensorFlow, and I agree... but reading this book allowed me to understand the structure of the underlying code enough to use Keras much more effectively than if I had just started there and never learned what's going on under the hood.
I was so impressed with the deep learning portion of this book that I went back and read the rest of it. I can't recommend this work highly enough.
The breadth of information covered if quite wide. The choice to start with Scikit-Learn was interesting, but makes sense on some level while he's introducing the more basic machine learning concepts. Simple machine learning techniques like logistic regression, data conditioning, dealing with training, validation, test set. Even if you've read about these concepts a million times, you might still glean useful information from these pages.
The Tensorflow section is also super well done. Straightforward setup instructions, pretty intelligible explanation of the basic concepts (variables, placeholders, layers, etc.) to get you started. The example code is quite good, and the notebooks are quite complete and seem to work well, with maybe a few tweaks and additional setup for some. I also found that the notebooks show more examples than what's in the book, which can be nice.
I only went really hands on with the reinforcement learning notebook, and found that it was well done and a good base to start my own work from. Even just having a section on reinforcement learning is very rare in a book of this style, and Geron's samples and explanations are really solid. He obviously has a strong grasp of many varied fields within deep learning, and that includes reinforcement learning. The only thing I wish it had was an A3C sample, to make my life that much easier. But you can't have everything.
I really liked his tips on which types of layers, activations, regularization, etc. are most effective, and gives good starting points for decent convergence. His explanation of multi-GPU Tensorflow was also quite good. The Tensorboard section was also very useful.
In short, if you want ONE book to get you into machine learning, and Tensforlow is on your radar, you can't go wrong with this one. Highly recommended!
Most recent customer reviews
This is not the second edition and book prints quality is not as good as a second edition i borrowed...Read more