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Deep Learning with PyTorch
Installation
Quick Reference
Tensors & Devices
Data Management
Models
Training
Evaluation
Diagnostics
Convolutional Networks
Residual Networks
Transfer Learning
Lessons
1. Supervised Learning
Guided Lab
2. Neural Networks
Guided Lab
3. Training Workflow
Guided Lab
4. Performance Analysis
Guided Lab
5. Convolutional Networks
Guided Lab
6. Deeper Architectures
Guided Lab
7. Representation Learning
Tutorials (PyTorch)
NumPy
Arrays
Operations
Indexing
Linear algebra
More exercises
PyTorch
Tensors
Automatic Differentiation
GPU Support
Neural Networks (MLP)
Dataset & DataLoader
Building a Neural Network
Training a Neural Network
Evaluating a Neural Network
Quick Recap
Convolutional Networks (CNN)
Network Architecture
Dataset From Files
Data Augmentation
Transfer Learning
Pretrained Models
Feature Extraction
Fine-Tuning
Conclusion
Deep Metric Learning
Siamese Network
Triplet Loss
Weak Supervision
Projects
How to Work on Projects
Image Classification
Overview
Image Scraping
Image Retrieval
Choice 1: Colors
Dataset
Feature Extraction
Training
Image Retrieval
Choice 2: Face Attributes
Dataset
Neural Networks
Loss function
Image retrieval
Troubleshooting
Choice 3: Sketches
Dataset
Overview
Image retrieval
Extensions
Object Localization
Part 1: Single Object
Dataset
Pretraining
Training
Evaluation
Part 2: Multiple Objects
Data Preparation
Model Architecture
Loss function
Evaluation
Crowd Counting
Choice 1: Density Map
Dataset
Preprocessing
Model Architecture
Training
Evaluation
Choice 2: Head localization
Dataset
Preprocessing
Model Architecture
Anchor Points
Training
Evaluation
Index
