Git Product home page Git Product logo

deeplearningbookcode-volume2's Introduction

"Deep Learning - From Basics to Practice" by Andrew Glassner

Books available from Amazon

Buy Volume 1

Buy Volume 2

Free Kindle Apps

The books are available from Amazon in Kindle form. Amazon makes a free Kindle reader for practically anything with a screen. You can download them at https://www.amazon.com/kindle-dbs/fd/kcp

Jupyter notebooks and Figures

ABOUT THE NOTEBOOKS

Only three chapters in the book contain explicit code. Chapter 15 discusses the Scikit-Learn library, and Chapters 23 and 24 discuss the Keras library. All of the code in these chapters is in Python, and is provided in Jupyter notebooks.

Most other chapters in the book contain at least some figures that were generated procedurally using Python. Those chapters also have associated notebooks which are available here. Those notebooks are not part of the book itself, but are offered as a kind of "behind the scenes" look at how the figures were made for those who want to dig into the process. These notebooks are only lightly documented.

Because of their role as learning materials, all of the code in these notebooks was written to emphasize clarity over all other stylistic concerns. This means that much of it can be shortened, and probably made faster as well. Feel free to dig in, optimize, convert to other languages, or otherwise play with the code.

All the notebooks are released under the MIT license. Informally, you're free to do pretty much anything with the code, including using it in your own projects, or even including it in commercial projects, as long as you keep my copyright along with the code. While I strove for accuracy and correctness, there is no warranty that the code is bug-free or fit for any purpose.

Some notebooks work with images. The images I used in the book are included with the notebooks. See the section below on Figures for details on their licensing, and see the book for the URL where each image may be found. All images without an explicit citation in the book are by the author, and are released under the MIT license.

The code for Volumes 1 and 2 are each in their own GitHub repo:

Notebooks for Volume 1:

Notebooks for Volume 2:

ABOUT THE FIGURES

The book is illustrated with roughly 1000 original figures. Most were drawn by the author in Adobe Illustrator. A few were drawn in Photoshop. All are available here in PNG format.

Most of these images are saved at high resolution (300 dpi or better), so they are appropriate for presentations and talks even when projected to large size.

All of these figures are released under the MIT license, like the code. This means you're free to use them any way you like, as long as you keep the copyright associated with them somehow. Use them for your classes, reports, papers, presentations, whatever you like!

You're not required to attribute me or the book if you use these images, but I'd appreciate it if you would.

Some figures include photographs. Many of these are by the author. All other photos are from Wikiart, Wikimedia, or Pixabay. The book provides a citation and URL to the source of each of these images. The first two sites state that their images are in the public domain. All images selected from Pixabay are labeled as released under the Creative Commons CC0 license, and explicitly state, "Free for commercial use. No attribution required."

To help you better sort through and choose figures, thumbnails for all the figures are available.

Thumbnails for all figures:

The figures for Volumes 1 and 2 are each in their own GitHub repo:

Figures for Volume 1:

Figures for Volume 2:

OTHER RESOURCES

Any other resources will be available in their own repository.

Other Resources:

deeplearningbookcode-volume2's People

Contributors

blueberrymusic avatar pinkwerks avatar

Watchers

 avatar  avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.