Module 1

GETTING STARTED WITH PYTORCH

• The Course Overview
• Introduction to PyTorch
• Installing PyTorch on Linux and Windows
• Installing CUDA
• Introduction to Tensors and Variables
• Working with PyTorch and NumPy
• Working with PyTorch and GPU
• Handling Datasets in PyTorch
• Deep Learning Using PyTorch

TRAINING YOUR FIRST NEURAL NETWORK

• Building a Simple Neural Network
• Loss Functions in PyTorch
• Optimizers in PyTorch
• Training the Neural Network
• Saving and Loading a Trained Neural Network
• Training the Neural Network on a GPU

COMPUTER VISION – CNN FOR DIGITS RECOGNITION

• Computer Vision Motivation
• Convolutional Neural Networks
• The Convolution Operation
• Concepts - Strides, Padding, and Pooling
• Loading and Using MNIST Dataset
• Building the Model
• Training and Testing

SEQUENCE MODELS – RNN FOR TEXT GENERATION

• Sequence Models Motivation
• Word Embedding
• Recurrent Neural Networks
• Building a Text Generation Model in PyTorch
• Training and Testing

AUTOENCODER - DENOISING IMAGES

• Autoencoders Motivation
• How Autoencoders Work
• Types of Autoencoders
• Building Denoising Autoencoder Using PyTorch
• Training and Testing

REINFORCEMENT LEARNING – BALANCE CARTPOLE USING DQN

• Reinforcement Learning Motivation
• Reinforcement Learning Concepts
• DQN, Experience Replay
• The OpenAI Gym Environment
• Building the Cartpole Agent Using DQN
• Training and Testing

Module 2

FIRST STOP: A QUICK INTRODUCTION TO PYTORCH

• The Course Overview
• What Makes PyTorch Special?
• Installing PyTorch

SLEEPING UNDER THE STARS: IT'S A BIRD...IT'S A PLANE...IT’S A CNN?

• Problem: Detect a Specific Type of Object in an Image
• Quick Win: Using a Pretrained AlexNet Model for Beaver Detection
• Getting and Preparing Image Data
• Building, Training, and Testing Your Model
• Using Your Model to Detect Beavers and What’s Next?

GOING ABROAD: LANGUAGE DETECTION FOR FUN AND PROFIT WITH RNN

• Problem: Recognize the Language of a Specific Text
• Understanding and Preparing Language Data
• Building, Training, and Testing Your Model for Language Detection
• Using Your Model to Detect Languages and What’s Next?

MAKING FRIENDS: LOST IN TRANSLATION WITH LSTM

• Problem: Translate a Specific Text from One Language to Another
• Understanding and Preparing Dataset for Language Translation
• Building, Training, and Testing Your Models for Language Translation
• Using Your Models for Language Translation

GETTING SOME CULTURE: BECOMING A DEEP NEURAL PICASSO WITH DNN

• Problem: Extract Key Style Features from One Image and Use It on Another One
• Preparing Images for Style Transfer
• Building and Training Style Transfer Model

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