LFD473 PyTorch in Practice: An Applications-First Approach

Introduction

• Who You Are
• Who we are
• Copyright and No Confidential Information
• Training
• Certification Programs and Digital Badging

PyTorch, Datasets, and Models

• What is PyTorch
• The PyTorch Ecosystem
• Supervised vs Unsupervised Learning
• Software Development vs Machine and Deep Learning
• 'Hello Model'
• Naming Is Hard
• Setup and Environment

Building Your First Dataset

• Tensors, Devices, and CUDA
• Datasets
• Dataloaders
• Datapipes
• Lab 1A: Non-Linear Regression

Training Your First Model

• Recap
• Models
• Loss Functions
• Gradients and Autograd
• Optimizers
• The Raw Training Loop
• Evaluation
• Saving and Loading Models
• NonLinearities
• Lab 1B: Non-Linear Regression

Building Your First Datapipe

• A New Dataset
• Lab 2: Price Prediction
• Tour of High Level Libraries

Transfer Learning and Pretrained Models

• What is Transfer Learning?
• Torch Hub
• Computer Vision
• Dropout
• ImageFolder Dataset
• Lab 3: Classifying Images

Pretrained Models for Computer Vision

• PyTorch Image Models
• HuggingFace

Natural Language Processing

• Natural Language Processing
• One Logit or Two Logits?
• Cross-Entropy Loss
• TensorBoard
• Lab 4: Sentiment Analysis
• Hugging Face Pipelines
• Generative Models

Fine-Tuning Pretrained Models for Computer Vision

• Fine Tuning Pretained Models
• Zero-shot Image Classification

Serving Models with TorchServe

• Archiving and Serving Models
• TorchServe

Datasets and Transformations for Object Detection and Image Segmentation

• Object Detection, Image Segmentation, and Keypoint Detection
• Bounding Boxes
• Torchvision Operators
• Transforms (V2)
• Custom Dataset for Object Detection
• Lab 5A: Fine-Tuning Object Detection Models

Models for Object Detection and Image Segmentation

• Models
• Lab 5B: Fine-Tuning Object Detection Models

Models for Object Detection Evaluation

• Recap
• Making Predictions
• Evaluation
• YOLO
• HuggingFace Pipelines for Object Detection
• Zero-Shot Object Detection

Word Embeddings and Text Classification

• Torchtext
• AG News Dataset
• Tokenization
• Embeddings
• Vector Databases
• Zero-Shot Text Classification
• Chunking Strategies
• Lab 6: Text Classification using Embeddings

Contextual Word Embeddings with Transformers

• Attention is All You Need
• Transformer
• An Encoder-Based Model for Classification
• Contextual Embeddings

Huggingface Pipelines for NLP Tasks

• HuggingFace Pipelines
• Lab 7: Document Q&A

Question and Answer, Summarization, and LLMs

• EDGAR Dataset
• Hallucinations
• Asymmetric Semantic Search
• ROUGE Score
• Decoder-Based Models
• Large Language Models (LLMs)

Closing and Evaluation Survey

• Evaluation Survey