PyTorch in Practice: An Applications-First Approach (LFD473)

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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

Image Classification with Torchvision



Torchvision
Pretrained Models as Feature Extractors



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
ab 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

Cílová skupina

This course is designed for machine learning practitioners who want to add deep learning models in PyTorch – especially pretraining models for Computer Vision and Natural Language Processing – to quickly protype and deploy applications.

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