hunkim/PyTorchZeroToAll
PyTorchZeroToAll
PyTorchZeroToAll is an educational resource and collection of tutorials focused on deep learning and the PyTorch framework. It provides a structured learning path for implementing neural network architectures, ranging from basic language syntax and fundamentals to complex model design.
The project serves as an implementation guide for building various network types, including linear, logistic, convolutional, and recurrent networks. It specifically covers the workflow for sequence modeling through the use of attention mechanisms and character-level networks.
The resource also covers machine learning data preparation, including the use of custom dataset loaders, text processing tools, and the translation of raw source files into tensors for training pipelines.
Features
- Deep Learning Fundamentals - Offers a structured learning path covering deep learning fundamentals, basic syntax, and model implementation.
- Deep Learning Implementations - Provides manual implementations of various neural network architectures including linear, convolutional, and recurrent networks.
- Neural Network Implementation Guides - Provides practical implementation guides for translating deep learning mathematical concepts into executable PyTorch code.
- PyTorch Model Development - Guides the design, implementation, and training of various neural networks using the PyTorch library.
- Sequence Learning Models - Implements advanced sequence learning models using attention mechanisms and character-level networks.
- Deep Learning Education - Serves as a curated educational resource for learning neural network theory and practice.
- PyTorch Deep Learning Examples - Offers a collection of PyTorch deep learning examples and step-by-step guides for building models.
- Machine Learning Learning Paths - Provides a structured learning path progressing from deep learning fundamentals to advanced sequence models.
- Input Sequence Attentions - Implements attention mechanisms that weight input sequence positions to improve long-range dependency learning.
- Data Preparation Tools - Includes tools and tutorials for cleaning and transforming raw data into PyTorch tensors.
- Dataset Loaders - Provides dataset loaders and text processing tools to prepare data for machine learning pipelines.
- Modular Layer Compositions - Teaches modular layer compositions by stacking neural network components into sequential data flows.
- Character-Level Models - Provides character-level tokenization and modeling to capture fine-grained linguistic patterns.
- Mini-Batch Processing - Covers mini-batch processing techniques to stabilize gradient descent and manage memory during training.
- Custom Dataset Loading - Demonstrates how to implement custom dataset loading classes to translate raw files into PyTorch tensors.
- Dataset Preparation Tutorials - Ships tutorials on formatting and processing raw text data for machine learning ingestion.
- Learning and Reference - PyTorch tutorials.
- Deep Learning Frameworks - Beginner-friendly video series and code for learning PyTorch.
- Tutorials - Listed in the “Tutorials” section of the The Incredible Pytorch awesome list.
Star history
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