Projects sharing features with Pytorch Meta Optimizer

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)

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

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

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

A PyTorch implementation of DenseNet.

Classification with PyTorch.

This project is a TensorFlow-based neural style transfer tool and deep learning image processor. It uses convolutional neural networks to apply the artistic style of one image to the content of another through neural image synthesis. The system supports multi-style blending to combine artistic characteristics from several different images into a single output. It also includes color-preserving stylization, which maintains the original color palette of the source image by merging source color data with the luminance of the stylized result. The tool provides capabilities for style abstraction