Open-source alternatives to E2e Model Learning

A PyTorch implementation of Learning to learn by gradient descent by gradient descent

PyTorch implementation of the Value Iteration Networks (VIN) (NIPS '16 best paper)

Pytorch implementation of DeepMind's differentiable neural computer paper.

This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b

Hierarchical Attentive Recurrent Tracking

Search and filter videos based on objects that appear in them using convolutional neural networks

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

:speaker: Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

:unlock: Lip Reading - Cross Audio-Visual Recognition using 3D Architectures

A PyTorch implementation of DenseNet.

Classification with PyTorch.

PyTorch-Pose is a PyTorch implementation of the general pipeline for 2D single human pose estimation. The aim is to provide the interface of the training/inference/evaluation, and the dataloader with various data augmentation options for the most popular human pose databases (e.g., the MPII…