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udacity/deep-learning

A collection of Jupyter notebook tutorials and projects for the Udacity Deep Learning Nanodegree Foundation program, covering neural networks, RNNs, GANs and more.

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What is it?

This repository holds the tutorial notebooks and project notebooks used in Udacity's Deep Learning Nanodegree Foundation program. Most tutorials walk through implementing models such as convolutional networks, recurrent networks and GANs, while others cover topics like weight initialization and batch normalization.

The tutorials range from a sentiment analysis model built in NumPy to introductions to TensorFlow, TFLearn and TensorBoard. Further notebooks cover autoencoders, transfer learning with VGG, Word2Vec embeddings, sequence-to-sequence models and deep Q-learning on an OpenAI Gym game.

The project notebooks include a bike rental predictor in NumPy, CIFAR-10 image classification, text generation, English-to-French machine translation and face generation with a DCGAN on CelebA. Each directory ships its own requirements.txt, and Conda environment files are provided in the environments folder.

Key features

  • 01Tutorial notebooks covering CNNs, RNNs, GANs, autoencoders and reinforcement learning
  • 02Implementations in NumPy, TensorFlow and TFLearn
  • 03Five project notebooks, including image classification and machine translation
  • 04Per-directory requirements.txt files for pip installs
  • 05Conda environment files, with separate GPU variants

Use cases

  • Learning deep learning by implementing models step by step in Jupyter
  • Studying TensorFlow basics through introductory notebooks
  • Practicing with GAN, RNN or Word2Vec implementations
  • Working through the Nanodegree's projects outside the course

Quick start

  1. 1

    Pick a tutorial or project folder from the table of contents

  2. 2

    Install that folder's dependencies with pip

    pip3 install -r requirements.txt

Things to know

  • Conda environment files are platform dependent, so you may need the one matching your system.
  • The material is built around the Udacity Nanodegree program and is a collection of notebooks, not a packaged library.

In collections

Learning & education tools

Topics

#deep-learning#jupyter#tensorflow#neural-networks#tutorial#gan#rnn#education

FAQ

What is deep-learning?
A collection of Jupyter notebook tutorials and projects for the Udacity Deep Learning Nanodegree Foundation program, covering neural networks, RNNs, GANs and more. This repository holds the tutorial notebooks and project notebooks used in Udacity's Deep Learning Nanodegree Foundation program. Most tutorials walk through implementing models such as convolutional networks, recurrent networks and GANs, while others cover topics like weight initialization and batch normalization.
Is deep-learning free to use? What license does it have?
deep-learning is open source under the MIT license. Check the license terms before using it in commercial products.
Is deep-learning still maintained?
Actively maintained. The last commit to deep-learning was on Oct 27, 2026.
What can I use deep-learning for?
Learning deep learning by implementing models step by step in Jupyter Studying TensorFlow basics through introductory notebooks Practicing with GAN, RNN or Word2Vec implementations Working through the Nanodegree's projects outside the course
How do I install deep-learning?
Pick a tutorial or project folder from the table of contents Install that folder's dependencies with pip: pip3 install -r requirements.txt
Where can I get deep-learning?
The source code and installation instructions are on GitHub: https://github.com/udacity/deep-learning

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