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.
- Your First Neural Network: Implement a neural network in Numpy to predict bike rentals.
- Implementing Gradient Descent: Introduction to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.
- Implement Linear Regression
- Sentiment Analysis: Neural networks for sentiment analysis.
- Keras: Hands-on introduction to Keras
- Intro to TensorFlow: Starting building neural networks with Tensorflow.
Each directory has a requirements.txt describing the minimal dependencies required to run the notebooks in that directory.
To install these dependencies with pip, you can issue pip3 install -r requirements.txt.
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".