Welcome to the Deep Learning Toolkit repository powered by PyTorch! This repository is designed to be your go-to resource for a wide range of deep learning functionalities and architectures, providing you with the tools you need to kickstart your projects or expand your expertise.
We have implemented a comprehensive set of deep learning functionalities, including but not limited to:
- Linear Layer: Implement linear transformation layers for your neural networks.
- Convolutional Layers: Apply convolutional layers for image processing tasks.
- Upsampling Layers: PixelUnshuffle, PixelShuffle, Linear, Bilinear interpolations, Transposed convolutions.
- Recurrent Layers: Explore Recurrent Neural Networks (RNNs), including LSTMs and GRUs.
- Optimizers: Choose from various optimization algorithms such as Adam,AdamW,SGD,SparseAdam,AdaGrad,RMSprop to train your models effectively.
- Loss Functions: Use different loss functions such as contrastive losses, classification losses, regression losses, meta-task losses tailored to your specific problem.
- Self-Attention: Implement self-attention mechanisms for sequence modeling & Vision Tasks.
- Multi-Head Attention: Utilize multi-head attention for your transformer models.
- Temporal Attention: Capture temporal patterns in your data.
- Stochastic Attention with Gumbel Softmax: Advanced attention techniques.
- Transformer Architecture: Harness the power of the transformer model for diverse applications.
- GANs (Generative Adversarial Networks): Generate data, images, and more with GANs.
- Reverse Automatic Differentiation: Customize gradients for specialized tasks.
- Activation Functions: Choose from a variety of activation functions, including ReLU, Sigmoid, and more.
- Dropout: Apply dropout as a regularization technique to prevent overfitting.
- Layer Norm: Use layer normalization for more stable training.
- BatchNorm: Incorporate batch normalization to accelerate your training process.
Our repository covers a spectrum of deep learning architectures, including:
- MLPs (Multi-Layer Perceptrons): Versatile feedforward networks.
- CNNs (Convolutional Neural Networks): Ideal for image processing and feature extraction.
- ResNets (Residual Networks): Deep architectures for improved gradient flow.
- LSTMs (Long Short-Term Memory): Perfect for sequence modeling and text generation.
- GRUs (Gated Recurrent Units): A simpler RNN variant.
- Transformer: The renowned transformer model, essential for NLP tasks.
- GAN (Generative Adversarial Network): Explore generative modeling.
In addition to core deep learning functionalities, we've also provided essential PyTorch utilities, including:
- BatchNorm2D: Batch normalization for 2D data.
- MeanPool2D: Average pooling for 2D data.
- MaxPool2D: Max pooling for 2D data.
- LayerNorm: Layer normalization for neural networks.
- Packed Sequences: A utility for handling variable-length data in sequence modeling.
Feel free to explore these components and architectures to supercharge your deep learning projects.
To get started with this repository, clone it to your local machine and follow the installation and usage instructions in the documentation. You can find detailed guides for each functionality and architecture in our wiki.
We welcome contributions from the community! If you have any improvements, bug fixes, or new functionalities to add, please open a pull request and join our mission to enhance the deep learning toolkit.
This project is licensed under the MIT License. See the LICENSE file for more details.
We hope you find this repository valuable for your deep learning journey. Feel free to star the project if you find it useful and share it with your fellow deep learning enthusiasts!
Happy coding!