Search Results for "deep learning ai"
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Showing 510 open source projects for "deep learning ai"
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Colossal-AI
Making large AI models cheaper, faster and more accessible
...There is an urgent demand to train models in a distributed environment. However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. It remains a challenge for AI researchers to implement complex distributed training solutions for their models. Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. -
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AI_Tutorial
A selection of learning materials, search, recommendation, advertising
AI_Tutorial is a large curated repository that aggregates high-quality learning resources related to artificial intelligence, machine learning, deep learning, natural language processing, and data engineering. The project functions as a centralized knowledge base designed to help engineers and researchers discover tutorials, technical articles, algorithm explanations, and architecture discussions from across the AI ecosystem. -
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Deep Learning Interviews book
Hundreds of fully solved job interview questions
The interviews.ai repository hosts the open materials for the book Deep Learning Interviews, a comprehensive collection of technical questions and fully solved problems covering many aspects of artificial intelligence. The project was created to help students, researchers, and engineers prepare for machine learning and deep learning interviews by providing structured explanations of key concepts. The repository organizes problems across topics such as neural networks, optimization, probabilistic models, and mathematical foundations of machine learning. -
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The Machine & Deep Learning Compendium
List of references in my private & single document
The Machine & Deep Learning Compendium is an open-source knowledge repository that compiles summaries, references, and learning materials related to machine learning and deep learning. The project functions as a comprehensive compendium that organizes hundreds of topics covering algorithms, frameworks, research areas, and practical machine learning workflows. -
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AI-Tutorials/Implementations Notebooks
Codes/Notebooks for AI Projects
AI-Tutorials/Implementations Notebooks repository is a comprehensive collection of artificial intelligence tutorials and implementation examples intended for developers, students, and researchers who want to learn by building practical AI projects. The repository contains numerous Jupyter notebooks and code samples that demonstrate modern techniques in machine learning, deep learning, data science, and large language model workflows. -
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learning
A log of things I'm learning
The learning repository by Amit Chaudhary is a continuously updated log of concepts, technologies, and skills related to software engineering and computer science. Rather than being a traditional software library, the repository acts as a structured knowledge base documenting the author’s ongoing learning journey across topics such as programming, system design, machine learning, and generative AI. -
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ONNX
Open standard for machine learning interoperability
ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. Open Neural Network Exchange (ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ... -
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Learn PyTorch for Deep Learning
Materials for the Learn PyTorch for Deep Learning
Learn PyTorch for Deep Learning is an open-source educational repository that provides the full learning materials for the “Learn PyTorch for Deep Learning: Zero to Mastery” course created by Daniel Bourke. The project is designed to teach beginners how to build deep learning models using PyTorch through a hands-on, code-first learning approach. Instead of focusing heavily on theory alone, the repository encourages learners to experiment with code and develop practical machine learning skills through guided examples and exercises. ... -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
...Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real systems that incorporate machine learning, large language models, data pipelines, and AI infrastructure. The curriculum includes a progression of topics such as foundational AI engineering skills, machine learning systems design, large language model usage, retrieval-augmented generation systems, model fine-tuning, and autonomous AI agents. ... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Unity ML-Agents Toolkit
Unity machine learning agents toolkit
Train and embed intelligent agents by leveraging state-of-the-art deep learning technology. Creating responsive and intelligent virtual players and non-playable game characters is hard. Especially when the game is complex. To create intelligent behaviors, developers have had to resort to writing tons of code or using highly specialized tools. With Unity Machine Learning Agents (ML-Agents), you are no longer “coding” emergent behaviors, but rather teaching intelligent agents to “learn” through a combination of deep reinforcement learning and imitation learning. ... -
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satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. -
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MONAI
AI Toolkit for Healthcare Imaging
The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm. Project MONAI also includes MONAI Label, an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create annotated datasets, and build AI models in a standardized MONAI paradigm. ... -
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides native Java development experience and functions like any other regular Java library. You don't have to be a machine learning/deep learning expert to get started. -
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. ... -
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AlphaTree
DNN && GAN && NLP && BIG DATA
...The repository organizes these architectures into a structured learning path that helps learners understand how deep learning models improved over time through changes in depth, architectural complexity, and training techniques. In addition to neural networks used for image classification, the project also references broader AI fields such as generative adversarial networks, natural language processing, and graph neural networks. -
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ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
...The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. Using deep learning models like CNN and RNN with financial and alternative data, and how to generate synthetic data with Generative Adversarial Networks, as well as training a trading agent using deep reinforcement learning. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
...Albumentations works well with data from different domains: photos, medical images, satellite imagery, manufacturing and industrial applications, Generative Adversarial Networks. Albumentations can work with various deep learning frameworks such as PyTorch and Keras. -
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TorchMetrics AI
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. -
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Burn
Burn is a new comprehensive dynamic Deep Learning Framework
Burn is a new comprehensive dynamic Deep Learning Framework from Tracel AI built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. Burn emphasizes performance, flexibility, and portability for both training and inference. Developed in Rust, it is designed to empower machine learning engineers and researchers across industry and academia. -
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CUDA Containers for Edge AI & Robotics
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
CUDA Containers for Edge AI & Robotics is an open-source project that provides a modular container build system designed for running machine learning and AI workloads on NVIDIA Jetson devices. The repository contains container configurations that package the latest AI frameworks and dependencies optimized for Jetson hardware. These containers simplify the deployment of complex machine learning environments by bundling libraries such as CUDA, TensorRT, and deep learning frameworks into reproducible container images. ... -
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AiLearning-Theory-Applying
Quickly get started with AI theory and practical applications
AiLearning-Theory-Applying is a comprehensive educational repository designed to help learners quickly understand artificial intelligence theory and apply it in practical machine learning and deep learning projects. The repository provides extensive tutorials covering mathematical foundations, machine learning algorithms, deep learning concepts, and modern large language model architectures. It includes well-commented notebooks, datasets, and implementation examples that allow learners to reproduce experiments and understand the inner workings of various algorithms. ... -
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Triton Inference Server
The Triton Inference Server provides an optimized cloud
Triton Inference Server is an open-source inference serving software that streamlines AI inferencing. Triton enables teams to deploy any AI model from multiple deep learning and machine learning frameworks, including TensorRT, TensorFlow, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton supports inference across cloud, data center, edge, and embedded devices on NVIDIA GPUs, x86 and ARM CPU, or AWS Inferentia. -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded, or automotive product platforms. ... -
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PyTorch
Open source machine learning framework
...PyTorch can be used as a replacement for Numpy, or as a deep learning research platform that provides optimum flexibility and speed. -
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OpenVINO
OpenVINO™ Toolkit repository
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more. Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. ...
