Useful Links
Computer Science
Artificial Intelligence
Deep Learning
PyTorch Library
1. Introduction to PyTorch
2. Tensors: The Foundation
3. Tensor Operations and Manipulation
4. Automatic Differentiation
5. Neural Network Construction
6. Data Handling and Processing
7. Model Training and Optimization
8. Model Persistence and Deployment
9. Advanced PyTorch Features
10. PyTorch Ecosystem Integration
Data Handling and Processing
Dataset Abstraction
Dataset Types
Map-style Datasets
Iterable-style Datasets
Dataset Interface
len Method
getitem Method
Iterator Protocol
Custom Dataset Creation
File-based Datasets
In-memory Datasets
Streaming Datasets
Dataset Composition
Concatenating Datasets
Subset Creation
Dataset Splitting
DataLoader Functionality
Batch Processing
Batch Size Configuration
Automatic Batching
Variable-length Sequences
Data Shuffling
Random Sampling
Reproducible Shuffling
Stratified Sampling
Parallel Data Loading
Multi-process Loading
Worker Process Management
Memory Sharing Considerations
Custom Collation
collate_fn Parameter
Handling Irregular Data
Padding Strategies
Memory Optimization
Pin Memory for GPU Transfer
Prefetching Strategies
Memory-mapped Files
Data Preprocessing
Transformation Pipelines
Transform Composition
Conditional Transforms
Random Transforms
Normalization Techniques
Statistical Normalization
Min-max Scaling
Standardization
Data Augmentation
Geometric Transformations
Color Space Modifications
Noise Addition
Missing Data Handling
Imputation Strategies
Masking Techniques
Robust Processing
Specialized Data Types
Image Data Processing
PIL Integration
OpenCV Compatibility
Multi-channel Images
Text Data Processing
Tokenization
Vocabulary Management
Sequence Padding
Audio Data Processing
Waveform Handling
Spectrogram Generation
Feature Extraction
Time Series Data
Windowing Techniques
Temporal Alignment
Missing Value Interpolation
Previous
5. Neural Network Construction
Go to top
Next
7. Model Training and Optimization