Deep Learning
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Official code repository for the 'Machine Learning with TensorFlow' book with practical examples.
An open-source library for training and deploying deep learning recommendation models with sparse data at scale using multi-GPU support.
A free course teaching diffusion models theory and hands-on implementation using Hugging Face's Diffusers library.
A Python library offering scalable and user-friendly implementations of state-of-the-art neural forecasting models.
A curated list of papers, datasets, and code for 3D point cloud analysis research, covering classification, segmentation, detection, and more.
A modular Python toolbox for state-of-the-art 6-DoF visual localization using hierarchical image retrieval and feature matching.
A family of open-source deep learning models for accurate biomolecular interaction and binding affinity prediction, rivaling AlphaFold3 and physics-based methods.
A web application for training deep learning models with a focus on computer vision tasks.
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Original implementation and hyperparameters for the 2014 paper "Generative Adversarial Networks" (GANs).
A fast parallel implementation of the Connectionist Temporal Classification (CTC) loss function for CPU and GPU.
An open-source cross-platform performance library of basic building blocks for deep learning applications, optimized for CPUs and GPUs.
A curated list of resources for action recognition, video understanding, object detection, and pose estimation in computer vision.
A curated collection of high-quality resources for quantitative and algorithmic trading with a focus on machine learning applications.
A Unity-based simulator for training self-driving car models using deep learning.
A curated list of satellite and aerial imagery datasets with annotations for computer vision and deep learning tasks.
A MATLAB/Octave toolbox for deep learning with implementations of neural networks, deep belief nets, autoencoders, and convolutional networks.
Intel's reference deep learning framework designed for high performance across CPUs, GPUs, and custom hardware.
A curated list of 100 foundational and influential papers in natural language processing for students and researchers.
Enables distributed TensorFlow training and inferencing on Apache Spark and Hadoop clusters with minimal code changes.
A JAX library for rapid prototyping of large-scale attention-based vision models across images, video, audio, and multimodal data.
A collection of handwritten notes, notebooks, and resources for Andrew Ng's Deep Learning Specialization on Coursera.
A Python library for self-supervised learning on images, providing a modular PyTorch-like framework with support for modern SSL models.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
A comprehensive Python-first reinforcement learning framework with modular abstractions for decision intelligence applications.
A TensorFlow project template with a well-designed folder structure and OOP design to accelerate deep learning development.
A curated collection of research papers and resources on Vision Transformers (ViT) for computer vision tasks.
A curated list of Python software for data science, covering machine learning, deep learning, visualization, and data manipulation.
Models, data loaders and abstractions for language processing, powered by PyTorch
A neural network that automatically adds color to grayscale images using deep learning techniques.
A web-based IDE for machine learning and data science with pre-installed libraries and tools, deployable via Docker.
A curated list of Generative AI tools, models, artworks, and educational resources.
Seamlessly integrate large language models like ChatGPT into scikit-learn for enhanced text analysis tasks.
A PyTorch-based framework for visual object tracking and video object segmentation, featuring implementations of state-of-the-art trackers like TaMOs, RTS, and DiMP.
A comprehensive collection of machine learning tutorials and implementations in Python, covering algorithms from scratch to production deployment.
Human Activity Recognition using TensorFlow and LSTM RNNs on smartphone sensor data to classify six movement types.
Human Activity Recognition using TensorFlow and LSTM RNNs on smartphone sensor data to classify six movement types.
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