Natural Language Processing

Introduction

• Introduction (slides, view, run)

• PyTorch Basics (slides, view, run)

Foundational Components 

• Perceptron (slides, view, run)

• Synthetic Data (slides, view, run)

• Sentiment Classification - Preprocessing (slides, view, run)

• Sentiment Classification with Perceptron (slides, view, run)

Feed Forward Networks

• Multilayer Perceptron (slides, view, run)

• Surname Classification - Preprocessing (slides, view, run)

• Surname Classification with MLP (slides, view, run)

• Convolutional Neural Networkds (slides, view, run)

• Surname Classification with CNN (slides, view, run)

Embedding Words and Types

• Word Embedding (slides, view, run)

• Word2Vec - Preprocessing (slides, view, run)

• Word2Vec - Skip-gram (slides, view, run)

Sequence Modeling

• Reccurent Neural Networks (slides, view, run)

• Sequence to Sequence Models (slides, view, run)

• Transformers (slides, view, run)

Introduction

• Basic Text Processing 1 (run)

• Basic Text Processing 2 (run)

Foundational Components

• Perceptron and Synthetic Data (run)

• SMS Classification - Preprocessing (run)

• SMS Classification with Perceptron (run)

Feed Forward Networks

• World Cities Classification - Preprocessing (run)

• World Cities Classification with MLP (run)

• World Cities Classification with CNN (run)

Embedding Words and Types

• Word2Vec - Skip-gram with Negative Sampling (run)

• Document Classification with Pretrained Embeddings (run)

Sequence Modeling

• Surname Classification with RNN (run)

• Surname Generation with RNN (run)

• Machine Translation with RNN (run)