Štruktúra kurzu
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
