Intro to Deep Learning with PyTorch

To succeed in this course, youa\x80\x99ll need to be comfortable with Python and data processing libraries such as NumPy and Matplotlib Basic knowledge of linear algebra and calculus is recommended, but isna\x80\x99t required to complete the exercises

Introduction to Deep LearningDiscover the basic concepts of deep learning such as neural networks and gradient descentImplement a neural network in NumPy and train it using gradient descent with in-class programming exercisesBuild a neural network to pred

Introduction to PyTorchHear 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

Deep Learning with PyTorchBuild your first neural network with PyTorch to classify images of clothingWork through a set of Jupyter Notebooks to learn the major components of PyTorchLoad a pre-trained neural network to build a state-of-the-art image class

Convolutional Neural NetworksUse PyTorch to build Convolutional Neural Networks for state-of-the-art computer vision applicationsTrain a convolutional network to classify dog breeds from images of dogs

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

Recurrent Neural NetworksBuild recurrent neural networks with PyTorch that can learn from sequential data such as natural languageImplement a network that learns from Tolstoy’s Anna Karenina to generate new text based on the novel

Natural Language ClassificationUse PyTorch to implement a recurrent neural network that can classify textUse your network to predict the sentiment of movie reviews

Deploying with PyTorchSoumith Chintala teaches you how to deploy deep learning models with PyTorchBuild a chatbot and compile the network for deployment in a production environment