Repo for the Deep Learning Nanodegree Foundations program.
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autoencoder
batch-norm
dcgan-svhn
embeddings
environments
face_generation
first-neural-network
gan_mnist
image-classification
intro-to-rnns
intro-to-tensorflow
intro-to-tflearn
language-translation
miniflow
minst-tensorflow
reinforcement
semi-supervised
sentiment-network
sentiment-rnn
seq2seq
tensorboard
transfer-learning
tv-script-generation
weight-initialization
.gitignore
.gitmodules
LICENSE
README.md

README.md

Deep Learning Nanodegree Foundation

This repository contains material related to Udacity's Deep Learning Nanodegree Foundation program. It consists of a bunch of tutorial notebooks for various deep learning topics. In most cases, the notebooks lead you through implementing models such as convolutional networks, recurrent networks, and GANs. There are other topics covered such as weight intialization and batch normalization.

There are also notebooks used as projects for the Nanodegree program. In the program itself, the projects are reviewed by Udacity experts, but they are available here as well.

Table Of Contents

Tutorials

Projects

  • Your First Neural Network: Implement a neural network in Numpy to predict bike rentals.
  • Image classification: Build a convolutional neural network with TensorFlow to classify CIFAR-10 images.
  • Text Generation: Train a recurrent neural network on scripts from The Simpson's (copyright Fox) to generate new scripts.
  • Machine Translation: Train a sequence to sequence network for English to French translation (on a simple dataset)
  • Face Generation: Use a DCGAN on the CelebA dataset to generate images of novel and realistic human faces.

Dependencies

Each directory has a requirements.txt describing the minimal dependencies required to run the notebooks in that directory.

pip

To install these dependencies with pip, you can issue pip3 install -r requirements.txt.

Conda Environments

You can find Conda environment files for the Deep Learning program in the environments folder. Note that environment files are platform dependent. Versions with tensorflow-gpu are labeled in the filename with "GPU".