1. Introduction to PyTorch and its applications in NLP

PyTorch is an open-source machine learning library developed by Facebook's AI Research lab. It's particularly well-suited for Natural Language Processing (NLP) tasks due to its dynamic computational graphs and intuitive design.

import torch

# Create a tensor
x = torch.tensor([1, 2, 3, 4, 5])
print(x)
                

2. Setting up the PyTorch environment

Learn how to install PyTorch on your local machine or use Google Colab for cloud-based development.

• Local Installation Guide
• Google Colab Setup

3. PyTorch basics

• Tensors and operations
• Autograd and computational graphs
• Building and using neural networks

4. Data processing for NLP

• Loading and preprocessing text data
• Creating datasets and dataloaders

5. Building a sentiment analysis model

• Implementing a simple neural network for text classification
• Forward pass and backward pass
• Training and evaluation process
• Hyperparameter tuning and optimization techniques

6. Named Entity Recognition

• Implementing a neural network for NER
• Training and evaluation process
• Optimization and customization
• Applications of NER

7. Advanced topics

• Word embeddings in PyTorch
• Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks
• Transfer learning with pre-trained models

Glossary of Essential PyTorch Terms