Intro to Deep Learning with PyTorch

What you will learn

1. Introduction to Deep Learning

   • Discover the basic concepts of deep learning such as neural networks and gradient descent
   • Implement a neural network in NumPy and train it using gradient descent with in-class programming exercises
   • Build a neural network to predict student admissions

2. Introduction to PyTorch

   • Hear from Soumith Chintala, the creator of PyTorch, how the framework came to be, where it’s being used now, and how it’s changing the future of deep learning

3. Deep Learning with PyTorch

   • Build your first neural network with PyTorch to classify images of clothing
   • Work through a set of Jupyter Notebooks to learn the major components of PyTorch
   • Load a pre-trained neural network to build a state-of-the-art image classifier

4. Convolutional Neural Networks

   • Use PyTorch to build Convolutional Neural Networks for state-of-the-art computer vision applications
   • Train a convolutional network to classify dog breeds from images of dogs

5. Style Transfer

   • Use a pre-trained convolutional network to create new art by merging the style of one image with the content of another image
   • Implement the paper "A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge"

6. Recurrent Neural Networks

   • Build recurrent neural networks with PyTorch that can learn from sequential data such as natural language
   • Implement a network that learns from Tolstoy’s Anna Karenina to generate new text based on the novel

7. Natural Language Classification

   • Use PyTorch to implement a recurrent neural network that can classify text
   • Use your network to predict the sentiment of movie reviews

8. Deploying with PyTorch

   • Soumith Chintala teaches you how to deploy deep learning models with PyTorch
   • Build a chatbot and compile the network for deployment in a production environment