dragen1860/deep-learning-with-tensorflow-book

★ 13,246Jupyter NotebookAudience · researcherComplexity · 2/5LicenseSetup · moderate

Mindmap

mindmap
  root((repo))
    What It Does
      Deep learning book
      Theory plus practice
      Chinese language
    Contents
      PDF ebook
      Jupyter notebooks
      Teaching slides
    Tech Stack
      Python
      TensorFlow 2.0
      Jupyter Notebook
    Audience
      Beginners
      University students
      Course instructors

mindmap root((repo)) What It Does Deep learning book Theory plus practice Chinese language Contents PDF ebook Jupyter notebooks Teaching slides Tech Stack Python TensorFlow 2.0 Jupyter Notebook Audience Beginners University students Course instructors

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Things people build with this

USE CASE 1

Work through the notebooks chapter by chapter to go from zero to building your first neural network.

USE CASE 2

Use the included slides and code examples to teach a university or bootcamp course on TensorFlow.

USE CASE 3

Run individual notebook examples to understand a specific deep learning concept like convolutions or RNNs.

Tech stack

PythonTensorFlowJupyter Notebook

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Python and TensorFlow 2.0 installed, a compatible Jupyter environment is needed to run the notebooks.

Non-commercial use only, share and use freely as long as you credit the author and link back to the GitHub page.

In plain English

This repository is an open-source book that teaches deep learning, a branch of artificial intelligence, using Google's TensorFlow 2.0 software. The book and its README are written in Chinese, and the author nicknames it the Dragon Book. It is aimed at beginners, and the README says it combines theory with hands-on practice so that newcomers can follow along. The repository is more than just text. According to the README it bundles the PDF e-book, the matching source code, and teaching slides. Some of the code examples have been converted into Jupyter notebooks, which are documents that mix written explanation with runnable code, and the author thanks a contributor for doing that conversion. The PDF can be downloaded from the repository or from a linked file-sharing service. Much of the README is about the book's reach and reputation. It notes that a printed edition is sold through major Chinese online stores, that a traditional-character version has been published in Taiwan, that the book has been covered by Chinese tech media, and that the repository spent several days ranked first on GitHub's global trending list. A long table lists universities that have adopted it as course material, and teachers are invited to email for the original slide files. The README also points to a wider set of related resources: a print edition, an introductory video, an English version of the book, paid online video courses, a companion book that uses the PyTorch framework instead of TensorFlow, and a separate repository of extra TensorFlow examples. Finally, the author asks that the material be used for non-commercial purposes only, with credit given to the author and a link back to the GitHub page. Feedback and corrections are requested through the GitHub issues page.

Copy-paste prompts

Prompt 1

I'm using the deep-learning-with-tensorflow-book notebooks. Walk me through the CNN chapter step by step and explain each layer.

Prompt 2

Using TensorFlow 2.0 as shown in deep-learning-with-tensorflow-book, help me build a model that classifies handwritten digits.

Prompt 3

I'm following deep-learning-with-tensorflow-book. My Jupyter notebook won't run the TensorFlow examples, here is the error: [paste error]. Fix it.

Prompt 4

Summarize the key deep learning concepts covered in deep-learning-with-tensorflow-book so I know what order to study them in.

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