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chiphuyen/stanford-tensorflow-tutorials

★ 10,382PythonAudience · researcherComplexity · 3/5LicenseSetup · hard

TLDR

Code examples from Stanford's CS 20 TensorFlow for Deep Learning Research course, covering deep learning, NLP, and chatbot construction in Python and TensorFlow 1.4.

Mindmap

mindmap
  root((repo))
    What it does
      Deep learning examples
      NLP tutorials
      Chatbot code
    Tech stack
      Python 3.6
      TensorFlow 1.4
    Use cases
      Course companion
      Research practice
    Audience
      Students
      Researchers
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Code map

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Things people build with this

USE CASE 1

Follow Stanford CS 20 deep learning examples locally to learn TensorFlow 1.4 techniques.

USE CASE 2

Study NLP and chatbot construction code from the course to practice deep learning patterns.

USE CASE 3

Reference the 2017 course materials alongside updated examples to see how TensorFlow evolved.

Tech stack

PythonTensorFlow

Getting it running

Difficulty · hard Time to first run · 1h+

Requires Python 3.6 and TensorFlow 1.4.1, both of which are old versions no longer maintained and difficult to install on modern systems.

Use, copy, modify, and distribute freely for any purpose, including commercial projects, as long as you keep the copyright notice.

In plain English

This repository holds the code examples that accompanied Stanford's CS 20 course, titled TensorFlow for Deep Learning Research. TensorFlow is a software library for building and training machine learning models, and the course was designed to teach researchers and students how to use it for deep learning projects. The examples are written in Python 3.6 and target TensorFlow version 1.4.1. The topics covered across the course materials include general deep learning techniques, natural language processing, and chatbot construction, based on the listed subject tags. Setup instructions and a list of required dependencies are included in the repository's setup folder. The repository also retains materials from an earlier 2017 version of the course, stored in a separate folder. Full lecture notes and the course syllabus were hosted at cs20.stanford.edu. The README is sparse and does not describe individual examples or explain what each file does. This appears to be primarily a companion resource for enrolled students following the course, rather than a standalone tutorial series designed to be read on its own. The code is released under the MIT license.

Copy-paste prompts

Prompt 1
Write a simple neural network in TensorFlow 1.4 following the Stanford CS 20 course style to classify images.
Prompt 2
Using the Stanford TensorFlow tutorials approach, build a seq2seq chatbot model in TensorFlow 1.4 with attention.
Prompt 3
Show me how to implement a word2vec embedding model using TensorFlow 1.4 as done in the Stanford CS 20 examples.
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