Deep Learning
Showing 36 of 402 projects
A visual roadmap outlining the skills, technologies, and learning paths to become an Artificial Intelligence expert in 2022.
A ready-to-use OCR Python library supporting 80+ languages and popular writing scripts like Latin, Chinese, Arabic, and Cyrillic.
A comprehensive collection of data science Python notebooks covering deep learning, machine learning, big data, visualization, and essential tools.
A curated repository of resources, tutorials, libraries, and tools for learning and applying data science to real-world problems.
A curated repository of resources, tutorials, libraries, and tools for learning and applying data science to real-world problems.
A curated repository of resources, tutorials, libraries, and tools for learning and applying data science to real-world problems.
A curated collection of papers and articles from companies sharing real-world data science and machine learning applications in production.
A top-down, hands-on daily study plan for software engineers transitioning into machine learning roles.
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A deep learning tool for upscaling and denoising anime-style images and photos using convolutional neural networks.
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
A curated list of awesome deep learning tutorials, projects, and communities.
A curated list of awesome deep learning tutorials, projects, and communities.
An open-source data labeling tool for annotating audio, text, images, videos, and time series with a simple UI and standardized output.
An open source neural network framework in C and CUDA, known for YOLO real-time object detection models.
Facebook AI Research's software system implementing state-of-the-art object detection algorithms like Mask R-CNN and RetinaNet.
A curated list of the top 100 most cited deep learning papers from 2012-2016, serving as a foundational reading list.
Jupyter notebooks with example code and exercises from the first edition of Hands-on Machine Learning with Scikit-Learn and TensorFlow.
A unified Python library for explaining any machine learning model's predictions using Shapley values from game theory.
The fastai book, published as Jupyter Notebooks, provides an introduction to deep learning, fastai, and PyTorch.
LaTeX code and Python interface for creating publication-quality neural network architecture diagrams.
An industrial deep learning framework supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
An industrial deep learning framework from China supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
A comprehensive library for building and training Graph Neural Networks (GNNs) with PyTorch.
A PyTorch library for building and training Graph Neural Networks (GNNs) on structured and irregular data.
A curated list of awesome computer vision resources, including papers, datasets, software, and tutorials.
A curated list of awesome computer vision resources, including papers, datasets, software, and courses.
A high-performance neural network inference framework optimized for mobile platforms, enabling efficient AI deployment on edge devices.
A lightweight Python library for face recognition and facial attribute analysis (age, gender, emotion, race) with a unified API.
A fast, memory-efficient reimplementation of OpenAI's Whisper speech-to-text model using CTranslate2.
A repository of examples, utilities, and best practices for building and deploying production-ready recommendation systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
An open standard format for representing machine learning models to enable interoperability between frameworks.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
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