This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
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Updated
May 3, 2024 - Jupyter Notebook
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
NN and CNN implementation from scratch in PyTorch with data loading, training, and evaluation phase on CIFAR 100 image dataset
Bidirectional, Expandable and Stacked Tagger --- BEaST. This package provides POS-tagging using a system composed of treetagger, spaCy and Stanza. Package also provides training of a new system provided the necessary files.
Pytorch implementation of "An intriguing failing of convolutional neural networks and the CoordConv solution" - https://arxiv.org/abs/1807.03247
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Deep Learning Tutorials with Pytorch
Bilinear attention networks for visual question answering
Pytorch implementation of Deep Siamese Convolutional Networks which gives the changes between old and new aerial images
A PyTorch Implementation for CondenseNet on Cifar10
A modern localization network architecture STN
A Linear Regression Model made with Pytorch
This repo contains a PyTorch implementation of a pretrained BERT model for multi-label text classification.
A pip-installable evaluator for GANs (IS and FID). Accepts either dataloaders or individual batches. Supports on-the-fly evaluation during training. A working DCGAN SVHN demo script provided.
The purpose of this project is to build a Denoising Diffusion Probabilistic Model and train it to generate images of Star Wars characters.
A python package made to streamline the usage of Variational Autoencoders, understand the algorithm first before using this package
PyTorch implementation of Super SloMo by Jiang et al.
PyTorch Adaptive Piecewise Linear Activation Function
PyTorch Implementation of DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning @ ECCV22
Fact Extraction and VERification baseline published in NAACL2018
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