Curriculum
- 1 Section
- 19 Lessons
- 4 Days
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- Topics19
- 1.1Introduction Who You Are Who we are Copyright and No Confidential Information Training Certification Programs and Digital Badging
- 1.2PyTorch, 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
- 1.3Building Your First Dataset Tensors, Devices, and CUDA Datasets Dataloaders Datapipes Lab 1A: Non-Linear Regression
- 1.4Training 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
- 1.5Building Your First Datapipe A New Dataset Lab 2: Price Prediction Tour of High Level Libraries
- 1.6Transfer Learning and Pretrained Models What is Transfer Learning? Torch Hub Computer Vision Dropout ImageFolder Dataset Lab 3: Classifying Images
- 1.7Pretrained Models for Computer Vision PyTorch Image Models HuggingFace
- 1.8Natural Language Processing Natural Language Processing One Logit or Two Logits? Cross-Entropy Loss TensorBoard Lab 4: Sentiment Analysis Hugging Face Pipelines Generative Models
- 1.9Image Classification with Torchvision Torchvision Pretrained Models as Feature Extractors
- 1.10Fine-Tuning Pretrained Models for Computer Vision Fine Tuning Pretained Models Zero-shot Image Classification
- 1.11Serving Models with TorchServe Archiving and Serving Models TorchServe
- 1.12Datasets 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
- 1.13Models for Object Detection and Image Segmentation Models Lab 5B: Fine-Tuning Object Detection Models
- 1.14Models for Object Detection Evaluation Recap Making Predictions Evaluation YOLO HuggingFace Pipelines for Object Detection Zero-Shot Object Detection
- 1.15Word Embeddings and Text Classification Torchtext AG News Dataset Tokenization Embeddings Vector Databases Zero-Shot Text Classification Chunking Strategies Lab 6: Text Classification using Embeddings
- 1.16Contextual Word Embeddings with Transformers Attention is All You Need Transformer An Encoder-Based Model for Classification Contextual Embeddings
- 1.17Huggingface Pipelines for NLP Tasks HuggingFace Pipelines Lab 7: Document Q&A
- 1.18Question and Answer, Summarization, and LLMs EDGAR Dataset Hallucinations Asymmetric Semantic Search ROUGE Score Decoder-Based Models Large Language Models (LLMs)
- 1.19Closing and Evaluation Survey Evaluation Survey
Fine-Tuning Pretrained Models for Computer Vision Fine Tuning Pretained Models Zero-shot Image Classification
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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
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