deep-learning-v2-pytorch

This repository holds the tutorial notebooks and project starter code for Udacity's Deep Learning Nanodegree program. Deep learning is a branch of machine learning where software learns to recognize patterns by passing data through many layers of mathematical operations, loosely inspired by how neurons connect in a brain. The course uses PyTorch, a popular toolkit that makes it practical to build and train these kinds of models in Python.

The material is organized into several topic areas. The neural networks section starts with the basics: how a network learns by adjusting its internal numbers in response to mistakes, illustrated through a project that predicts student admissions. From there, the course covers convolutional neural networks, which are particularly good at recognizing things in images. Exercises here include classifying handwritten digits and photos of common objects, as well as a style transfer project where a network applies the visual style of one image (like a painting) onto a different photo.

The recurrent networks section covers models that process sequences, such as chains of words or time-series data. One exercise trains a network to generate new TV show scripts in the style of Seinfeld. Another builds a sentiment classifier that reads a movie review and predicts whether it is positive or negative. There is also material on word embeddings, which are a way of representing words as numbers so that words with similar meanings end up near each other mathematically.

The generative adversarial networks section teaches how to train two networks against each other so that one learns to generate realistic-looking images. Projects here include generating synthetic human faces using a celebrity photo dataset.

Projects in the program are reviewed by human graders at Udacity, and the starting code for each project is included in this repository. Setup requires installing Anaconda, a Python environment manager, and the README includes a step-by-step guide for getting the dependencies installed on Linux, Mac, or Windows.