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Grokking Deep Learning First Edition

by Andrew Trask (Author)
4.5 4.5 out of 5 stars 161 ratings
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Summary

Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning.

About the Book

Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks.

What's inside

  • The science behind deep learning
  • Building and training your own neural networks
  • Privacy concepts, including federated learning
  • Tips for continuing your pursuit of deep learning

About the Reader

For readers with high school-level math and intermediate programming skills.

About the Author

Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform.

Table of Contents

  1. Introducing deep learning: why you should learn it
  2. Fundamental concepts: how do machines learn?
  3. Introduction to neural prediction: forward propagation
  4. Introduction to neural learning: gradient descent
  5. Learning multiple weights at a time: generalizing gradient descent
  6. Building your first deep neural network: introduction to backpropagation
  7. How to picture neural networks: in your head and on paper
  8. Learning signal and ignoring noise:introduction to regularization and batching
  9. Modeling probabilities and nonlinearities: activation functions
  10. Neural learning about edges and corners: intro to convolutional neural networks
  11. Neural networks that understand language: king - man + woman == ?
  12. Neural networks that write like Shakespeare: recurrent layers for variable-length data
  13. Introducing automatic optimization: let's build a deep learning framework
  14. Learning to write like Shakespeare: long short-term memory
  15. Deep learning on unseen data: introducing federated learning
  16. Where to go from here: a brief guide
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Previous page
  1. ISBN-10
    1617293709
  2. ISBN-13
    978-1617293702
  3. Edition
    First Edition
  4. Publisher
    Manning
  5. Publication date
    January 25, 2019
  6. Language
    English
  7. Dimensions
    7.38 x 0.7 x 9.25 inches
  8. Print length
    336 pages
  9. See all details
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From the Publisher

Grokking Deep Learning

About the Book

Artificial Intelligence is one of the most exciting technologies of the century, and Deep Learning is in many ways the 'brain' behind some of the world's smartest Artificial Intelligence systems out there. Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe.

About the Reader

Written for readers with high school-level math and intermediate programming skills. Experience with Calculus is helpful but not required.

What's Inside:

  • How neural networks 'learn'
  • You will build neural networks that can see and understand images
  • You will build neural networks that can translate text between languages and even write like Shakespeare
  • You will build neural networks that can learn how to play videogames

Grokking Algorithms Grokking Deep Learning
Customer Reviews
4.6 out of 5 stars
1,428
4.5 out of 5 stars
161
Price $25.19 $47.49

Editorial Reviews

About the Author

Andrew Trask is a PhD student at Oxford University, funded by the Oxford-DeepMind Graduate Scholarship, where he researches Deep Learning approaches with special emphasis on human language. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning where he trained the world's largest artificial neural network with over 160 billion parameters, and helped guide the analytics roadmap for the Synthesys cognitive computing platform which tackles some of the most complex analysis tasks across government intelligence, finance, and healthcare industries.

Product details

  • Publisher ‏ : ‎ Manning; First Edition (January 25, 2019)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 336 pages
  • ISBN-10 ‏ : ‎ 1617293709
  • ISBN-13 ‏ : ‎ 978-1617293702
  • Item Weight ‏ : ‎ 1.32 pounds
  • Dimensions ‏ : ‎ 7.38 x 0.7 x 9.25 inches
  • Customer Reviews:
    4.5 4.5 out of 5 stars 161 ratings

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Andrew W. Trask

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Customer reviews

4.5 out of 5 stars
4.5 out of 5
161 global ratings
Excellent Introduction to the Subject
5 Stars
Excellent Introduction to the Subject
Andrew W. Trask has reminded me that most of us need to know more about Deep Learning. His book is a great way for people to get up to speed on the basics. His explanations are clear and fairly easy to digest. I like that he addresses several known issues with deep learning while providing many useful examples. While this may not be the best book for people with a Ph. D. in mathematics, it's a great introduction for the rest of us. Enjoy!
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Top reviews from the United States

Joe C
5.0 out of 5 stars Great book to learn ML
Reviewed in the United States on October 6, 2023
Verified Purchase
This book came highly recommended to me by a colleague at work. I and others at work have agreed that it's a great primer to learn how machine learning works and how to build your first model. It walks through every step and detail with examples, code, and visuals to bring it all together.
2 people found this helpful
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Michael
5.0 out of 5 stars Excellent Introduction to the Subject
Reviewed in the United States on October 29, 2022
Verified Purchase
Andrew W. Trask has reminded me that most of us need to know more about Deep Learning. His book is a great way for people to get up to speed on the basics. His explanations are clear and fairly easy to digest. I like that he addresses several known issues with deep learning while providing many useful examples. While this may not be the best book for people with a Ph. D. in mathematics, it's a great introduction for the rest of us. Enjoy!
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Michael
5.0 out of 5 stars Excellent Introduction to the Subject
Reviewed in the United States on October 29, 2022
Andrew W. Trask has reminded me that most of us need to know more about Deep Learning. His book is a great way for people to get up to speed on the basics. His explanations are clear and fairly easy to digest. I like that he addresses several known issues with deep learning while providing many useful examples. While this may not be the best book for people with a Ph. D. in mathematics, it's a great introduction for the rest of us. Enjoy!
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Inna K.
5.0 out of 5 stars Excellent
Reviewed in the United States on March 16, 2021
Verified Purchase
I rarely write reviews, but I have to here. This is an excellent book. It's concise, clear, and does an excellent job of conveying understanding and intuition. Having read bits and pieces about neutral networks over the years, I'm glad I picked up this book and gained conceptual understanding.
2 people found this helpful
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Sam
4.0 out of 5 stars A good introductory book for getting you started into Deep Learning and AI in general
Reviewed in the United States on November 30, 2021
Verified Purchase
I'm going to be brief and list the good and the bad.

* Good:
1- Easy to read (one of the best books to get you started)
2- Hands on approach to implementing Neural networks
3- Some introduction to popular AI libraries such as Numpy
4- Good guidance on next steps

* Bad:
1- Syntax and coding problems that are easy to detect by a trained eye but not as easy for a novice learner
2- Some typographical errors throughout the book

I have to clarify that book on its own is good. I think there are probably some items that could have been taken care of (I mentioned above) through the editing process but hopefully the next versions of the books can take care of these issues.
One person found this helpful
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David Weiseth
5.0 out of 5 stars Dripping with Understanding
Reviewed in the United States on January 30, 2019
Verified Purchase
Just arrived and diving in this week, the first impressions are that this is a deep dive on the mechanisms of Deep learning, but exceptional in the way the material is accessible to those without classical math background. You just need to devote some effort and basic reasoning and you should be plenty out of this book, Bon appetit ! I will update this if my description changes, this study effort will take a few weeks. Peace.
32 people found this helpful
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Amazon Customer
5.0 out of 5 stars The book I wish I had when I started learning deep learning
Reviewed in the United States on February 5, 2019
Verified Purchase
This is a wonderful, plain-English discussion of the mechanics that go on under the hood of neural networks - from data flow to updating of weights. Specifically written without a slant on normally-wonky math, the concepts are presented and then advanced at a digestable pace for anyone. It makes for a wonderful textbook for a course, and should be required reading for product managers or marketing people getting into deep learning, alike.
20 people found this helpful
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Darius
1.0 out of 5 stars Not worth it. Only the first half of the book is useful.
Reviewed in the United States on June 15, 2023
Verified Purchase
I started reading this book as an introduction to deep learning. The first half of the book does a good job of explaining the concepts and code, but around chapter ten the book becomes confusing. I ended up having to use other recourses just to try and understand what the author was trying to explain. I ended up starting a different book recommend by a friend in this field. I think this book needs some revision in a lot of ways. I am not sure if the code became hard to read because they used numpy or poor explanations.
2 people found this helpful
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IOANNIS TSITSOYANNIS
5.0 out of 5 stars Fine
Reviewed in the United States on March 23, 2019
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Covered my expectations
4 people found this helpful
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Client Amazon
5.0 out of 5 stars Exellent
Reviewed in France on March 6, 2024
Verified Purchase
Pour moi c'est le meilleur livre pour comprendre le fondement de deep learning comment fonctionne a l'interieur
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masood h siddiqui
5.0 out of 5 stars Lovely book. Concepts well explained.
Reviewed in India on October 29, 2023
Verified Purchase
Awesome book on Deep Learning. It builds up concepts in a lucid manner. The coverage is comprehensive and logical. Must for any one who is interested in Deep Learning.
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GrimTrigger
4.0 out of 5 stars Invites to grok DL
Reviewed in Germany on March 25, 2021
Verified Purchase
Despite having a mathematical background (and having grokked many concepts of DL) I found this book insightful.

On some shortcomings:
I agree with some of the other reviewers that, having mastered the maths of DL, this approach might rather confuse. Maybe math is the simplest language in this regard. Personally I found a few points more distracting than this, specifically the backpropagation for the NN for sigmoids in chapter 11 (an approximation is used which is never explained) and the batch gradient descent in chapter 8 (just wrong). A minor point: Personally I prefer PEP8 coding standard, the code is also a bit unique and sometimes hard to read.

On its strengths:
This book invites to deal with DL deeply, try out for yourself, to learn from your mistakes (and maybe deliberately from shortcomings in the explanations). This is sometimes painful, yet this is the way you learn. Specifically the strongest points are the step-by-step explanations (e.g. backprop) where each step is executed "on paper". I very much liked the part on Autograd. Having Worked with PyTorch and TF for years I took many things for granted which, surprisingly, I really never understood. Building a framework step by step from ground up helped me a lot. An other great point are the many tips throughout the book (sometimes well hidden), which are also worded so that they invite deeper understanding (and more research into the topic).

Overall (for me) the strong points outweigh the shortcomings by a wide margin.
2 people found this helpful
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Daniel
5.0 out of 5 stars Incredible, intuitive and layman friendly
Reviewed in the United Kingdom on April 1, 2020
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The author has done an astounding job at distilling the essence and complexities of the topic into a format that is almost child’s play. At first this book for me was a supplementary read whilst I did the fast.ai course, but since has become the main focus and resource for learning. Thank you Andrew for bringing this topic to the layman and propelling me Into this field.
One person found this helpful
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5.0 out of 5 stars Core concepts simplified with great explanation
Reviewed in India on August 18, 2021
Verified Purchase
One of the best deep learning books which provide the clearest explanation of the mathematical foundation of deep learning. The explanation makes core concepts crystal clear.

I wish I read this book when I first started deep learning.

Thank you to the author for writing such a great book.
One person found this helpful
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