Search Results for "deep" - Page 22
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Showing 639 open source projects for "deep"
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MMSkeleton
A OpenMMLAB toolbox for human pose estimation, skeleton-based action
...MMSkeleton is developed on our research project ST-GCN. MMSkeleton provides a flexible framework for organizing codes and projects systematically, with the ability to extend to various tasks and scale up to complex deep models. MMSkeleton addresses to multiple tasks in human understanding. Build a custom skeleton-based dataset. Create your own applications. MMSkeleton is an OpenMMLAB toolbox for human pose estimation, skeleton-based action recognition, and action synthesis. -
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PyTracking
Visual tracking library based on PyTorch
A general python framework for visual object tracking and video object segmentation, based on PyTorch. Official implementation of the RTS (ECCV 2022), ToMP (CVPR 2022), KeepTrack (ICCV 2021), LWL (ECCV 2020), KYS (ECCV 2020), PrDiMP (CVPR 2020), DiMP (ICCV 2019), and ATOM (CVPR 2019) trackers, including complete training code and trained models. -
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Python Machine Learning
The "Python Machine Learning (2nd edition)" book code repository
This repository accompanies the well-known textbook “Python Machine Learning, 2nd Edition” by Sebastian Raschka and Vahid Mirjalili, serving as a complete codebase of examples, notebooks, scripts and supporting materials for the book. It covers a wide range of topics including supervised learning, unsupervised learning, dimensionality reduction, model evaluation, deep learning with TensorFlow, and embedding models into web apps. Each chapter has Jupyter notebooks and Python scripts that replicate the examples in the book, allowing readers to run, inspect, and tweak code directly as they follow material. The structure also includes errata documentation and assets (images) that appear in the printed edition, providing a rich supplement to learning. ... -
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cocoNLP
A Chinese information extraction tool
...The project blends pattern-based methods with NLP heuristics, giving developers dependable results for real-world texts like chats, comments, and user-generated content. Its API is intentionally simple, so you can drop it into scripts, ETL jobs, or dashboards without deep ML expertise. Because it aims at utility over complexity, it’s useful for prototyping data products or building lightweight text analytics where large models would be overkill. The repository also includes examples and test snippets to help you understand expected inputs and typical outputs, which shortens the learning curve for newcomers. -
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A Machine Learning Course with Python
A course about machine learning with Python
The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python. Machine Learning, as a tool for Artificial Intelligence, is one of the most widely adopted scientific fields. A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python. In this project, we built... -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few... -
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TensorFlow Course
Simple and ready-to-use tutorials for TensorFlow
This repository houses a highly popular (~16k stars) set of TensorFlow tutorials and example code aimed at beginners and intermediate users. It includes Jupyter notebooks and scripts that cover neural network fundamentals, model training, deployment, and more, with support for Google Colab. -
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Dragonfire
The open-source virtual assistant for Ubuntu based Linux distributions
Dragonfire is the open-source virtual assistant project for Ubuntu-based Linux distributions. Her main objective is to serve as a command and control interface to the helmet user. So that you will be able to give orders just by using your voice commands and your eye movements. That makes the helmet handsfree. We are planning to ship Dragonfire as a preinstalled software package on DragonOS Linux Distribution. DragonOS will be a Linux distribution specially designed for the helmet. It will... -
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Dive-into-DL-TensorFlow2.0
Dive into Deep Learning
This project changes the MXNet code implementation in the original book "Learning Deep Learning by Hand" to TensorFlow2 implementation. After consulting Mr. Li Mu by the tutor of archersama , the implementation of this project has been agreed by Mr. Li Mu. Original authors: Aston Zhang, Li Mu, Zachary C. Lipton, Alexander J. Smola and other community contributors. There are some differences between the Chinese and English versions of this book . -
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Image Quality Assessment
Convolutional Neural Networks to predict aesthetic quality of images
...The repository includes code for training models, performing inference, and evaluating predicted scores against labeled datasets. It also provides utilities for image preprocessing and data management that help prepare datasets for training deep learning models. -
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nGraph
nGraph has moved to OpenVINO
...We've also seen performance boosts running workloads that are not included on the list of Validated workloads, thanks to nGraph's powerful subgraph pattern matching. Additionally, we have integrated nGraph with PlaidML to provide deep learning performance acceleration on Intel, nVidia, & AMD GPUs. nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets. We strongly believe in providing freedom, performance, and ease of use to AI developers. Our documentation has extensive information about how to use nGraph Compiler stack to create an nGraph computational graph, integrate custom frameworks, and to interact with supported backends. -
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AI Cheatsheets
Essential Cheat Sheets for deep learning and machine learning research
cheatsheets-ai is an open-source repository that collects essential cheat sheets covering many tools and concepts used in machine learning, deep learning, and data science. The project aims to provide quick-reference materials that help engineers, researchers, and students review key techniques and frameworks without reading extensive documentation. It compiles cheat sheets for widely used libraries and technologies such as TensorFlow, Keras, NumPy, Pandas, Scikit-learn, Matplotlib, and PySpark. ... -
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MatchZoo
Facilitating the design, comparison and sharing of deep text models
The goal of MatchZoo is to provide a high-quality codebase for deep text matching research, such as document retrieval, question answering, conversational response ranking, and paraphrase identification. With the unified data processing pipeline, simplified model configuration and automatic hyper-parameters tunning features equipped, MatchZoo is flexible and easy to use. Preprocess your input data in three lines of code, keep track parameters to be passed into the model. -
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DeepSDF
Learning Continuous Signed Distance Functions for Shape Representation
DeepSDF is a deep learning framework for continuous 3D shape representation using Signed Distance Functions (SDFs), as presented in the CVPR 2019 paper DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation by Park et al. The framework learns a continuous implicit function that maps 3D coordinates to their corresponding signed distances from object surfaces, allowing compact, high-fidelity shape modeling. -
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Spotlight
Deep recommender models using PyTorch
Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models. -
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Activity Recognition
Resources about activity recognition
This repository is a curated collection of resources, papers, code, and summaries relating to human activity recognition/behavior recognition. It is not a single integrated software package but rather a knowledge base organizing feature extraction methods, deep learning approaches, transfer learning strategies, datasets, and representative research in behavior recognition. The repository includes links to code in MATLAB, Python, summaries of algorithms, datasets, and relevant research papers. Feature extraction method summaries (e.g. motion, sensor, vision). Deep learning for activity recognition references. -
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Resemblyzer
A python package to analyze and compare voices with deep learning
Resemblyzer is a Python package for analyzing and comparing voices with deep learning. It works by turning speech audio into a compact voice embedding that represents the speaker’s vocal characteristics. These embeddings can then be used for speaker similarity, clustering, diarization experiments, voice comparison, and audio dataset exploration. The project is useful for researchers and developers who need a practical way to reason about speaker identity without building a voice encoder from scratch. ... -
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Azure Machine Learning Python SDK
Python notebooks with ML and deep learning examples
Azure Machine Learning Python SDK is a curated repository of Python-based Jupyter notebooks that demonstrate how to develop, train, evaluate, and deploy machine learning and deep learning models using the Azure Machine Learning Python SDK. The content spans a wide range of real-world tasks — from foundational quickstarts that teach users how to configure an Azure ML workspace and connect to compute resources, to advanced tutorials on using pipelines, automated machine learning, and dataset handling. Because it is designed to work with Azure Machine Learning compute instances, many notebooks can be executed directly in the cloud without additional setup, but they can also run locally with the appropriate SDK and packages installed. ... -
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X-DeepLearning
An industrial deep learning framework for high-dimension sparse data
X-DeepLearning (XDL for short) is a complete set of deep optimization solutions for high-dimensional sparse data scenarios (such as advertising/recommendation/search, etc.). XDL version 1.2 has been released recently. Performance optimization for large batch/low concurrency scenarios, 50-100% performance improvement in such scenarios. Storage and communication optimization, parameters are automatically allocated globally without manual intervention, and requests are merged to completely eliminate computing/storage/communication hotspots of ps. ... -
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DCGAN in TensorLayerX
The Simplest DCGAN Implementation
This is an implementation of Deep Convolutional Generative Adversarial Networks. First, download the aligned face images from google or baidu to a data folder. Please place dataset 'img_align_celeba.zip' under 'data/celebA/' by default. -
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Girls-In-AI
Free learning code series: Xiaobai's introduction to Python
Girls-In-AI is an educational repository created to encourage women and beginners to learn programming and artificial intelligence through accessible tutorials and practice materials. The project provides a collection of beginner-friendly learning resources covering Python programming, data analysis, machine learning, and deep learning topics. It aims to lower the barrier to entry for people who want to enter the field of artificial intelligence by offering structured learning paths and practical examples. The repository includes Jupyter notebooks, tutorials, and exercises that guide learners through topics such as data processing, machine learning model development, and Kaggle competition practice. ... -
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Project Malmo
A platform for Artificial Intelligence experimentation on Minecraft
...That learns transferable skills throughout its existence, and applies them to solve new, challenging problems? Project Malmo sets out to address these core research challenges, addressing them by integrating (deep) reinforcement learning, cognitive science, and many ideas from artificial intelligence. The Malmo platform is a sophisticated AI experimentation platform built on top of Minecraft, and designed to support fundamental research in artificial intelligence. The Project Malmo platform consists of a mod for the Java version, and code that helps artificial intelligence agents sense and act within the Minecraft environment. ... -
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Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
Combining improvements in deep reinforcement learning. Results and pretrained models can be found in the releases. Data-efficient Rainbow can be run using several options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps). -
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MIT Deep Learning Book
MIT Deep Learning Book in PDF format by Ian Goodfellow
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville. -
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ChainerCV
ChainerCV: a Library for Deep Learning in Computer Vision
ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using Chainer. In ChainerCV, we define the object detection task as a problem of, given an image, bounding box-based localization and categorization of objects. Bounding boxes in an image are represented as a two-dimensional array of shape (R,4), where R is the number of bounding boxes and the second axis corresponds to the coordinates of bounding boxes. ChainerCV supports dataset loaders, which can...
