Deep Learning From Basics to Practice

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Deep Learning From Basics to PracticeI am 10 kinds of excited to announce that this labor of love is finally complete and ready for you to read!

It’s a complete and friendly guide for programmers, artists, scientists, engineers, musicians, and anyone else who wants to understand and use deep learning. Our principles are clear explanations, lots of great illustrations, and no math beyond addition and multiplication. The ideas are applicable to any language or library you want to use. You’ll know how to design, build, and use existing and original DL networks, and how to make them work for you. If you’re looking for a complete but gentle guide to deep learning, starting with no expectations and then bringing you up to today’s powerful systems, you’ve found it!

Buy the books now at Amazon for just $10 each!

Sneak Peak: Read the Backpropagation chapter draft right now for free!

Friendly Writing

The book uses the same friendly and lucid tone that thousands of readers have enjoyed in my other books, papers, and my computer graphics column.

Enthusiastically Illustrated

Good illustrations can share some ideas better than words. The book contains nearly 1000 expertly conceived and executed images. Visual thinkers, rejoice! If you need illustrations for your talks or papers, all the figures are available now, for free, for you to use any way you like! Hop over to the GitHub repositories for Volume 1 and Volume 2.

Language and Library Independent

Except for two practical chapters based on Python libraries, nothing is tied to any particular language or library. We’re all about the ideas, which apply to whatever system you want to use (including your own!).

No Prerequisites

If you can multiply and know how to write “Hello World” in any computer language, you’re ready for this book. Nothing else is assumed, and everything is included. If you want to get the most out of the two practical chapters, a bit of Python knowledge will go a long way.

No Math

Well, there’s some addition and multiplication, but that’s it! We don’t have any big equations or heavy math. Instead, we present everything with straightforward discussions and examples, and tons of images.

Jupyter Notebooks Included

For those into Python, we include over 70 Jupyter notebooks! They contain the code for the practical chapters on scikit-learn and Keras, and also give you the code to make every computer-generated figure in the book. You can even download the notebooks right now, for free. There’s a GitHub repository for Volume 1, and another for Volume 2.

Table of Contents: Volume 1

  1. 1 Introduction to Machine Learning
  2. 2 Statistics
  3. 3 Probability
  4. 4 Bayes’ Rule
  5. 5 Curves And Surfaces
  6. 6 Information Theory
  7. 7 Classification
  8. 8 Training And Testing
  9. 9 Overfitting And Underfitting
  10. 10 Neurons
  11. 11 Learning And Reasoning
  12. 12 Data Preparation
  13. 13 Classifiers
  14. 14 Ensembles
  15. 15 Scikit-Learn
  16. 16 Feed Forward Networks
  17. 17 Activation Functions
  18. 18 Backpropagation
  19. 19 Optimizers

Table of Contents: Volume 2

  1. 20 Deep Learning
  2. 21 Convolutional Neural Nets (CNNs)
  3. 22 Recurrent Nerual Nets (RNNs)
  4. 23 Keras Part 1
  5. 24 Keras Part 2
  6. 25 Autoencoders
  7. 26 Reinforcement Learning
  8. 27 Generative Adversarial Networks (GANs)
  9. 28 Creative Applications
  10. 29 Datasets
  11. 30 Glossary

The books are available now!

Buy Volume 1 and Buy Volume 2.