Search Results for "learning" - Page 28
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Showing 1132 open source projects for "learning"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Computer Vision
Best Practices, code samples, and documentation for Computer Vision
In recent years, we've see an extra-ordinary growth in Computer Vision, with applications in face recognition, image understanding, search, drones, mapping, semi-autonomous and autonomous vehicles. A key part to many of these applications are visual recognition tasks such as image classification, object detection and image similarity. This repository provides examples and best practice guidelines for building computer vision systems. The goal of this repository is to build a comprehensive... -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than... -
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PyText
A natural language modeling framework based on PyTorch
PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. -
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DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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SageMaker Containers
Create SageMaker-compatible Docker containers
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. -
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log.c
A simple logging library implemented in C99
log.c is a compact logging library implemented in C99 for projects that need useful runtime diagnostics without pulling in a large dependency. It provides six familiar logging levels, from trace through fatal, and uses printf-style formatting so developers can add structured messages with minimal learning curve. By default, it writes readable log lines to stderr with timestamps, levels, source file names, and line numbers. It also supports quiet mode, configurable log levels, file outputs, custom callbacks, and optional thread locking for multi-threaded programs. Developers can enable ANSI color output at compile time to make terminal logs easier to scan. ... -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. -
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Flutter Example Apps
Basic Flutter apps, for flutter devs
...This repository is frequently starred and forked by developers because it provides a broad, hands-on showcase of how different features of the Flutter framework and Dart language are used in practice. Each example typically links to online resources or videos that walk through how the app was built, making it both a reference collection and a learning tool for beginners and intermediates alike. -
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Albedo
A recommender system for discovering GitHub repos
Albedo is an open-source recommender system aimed at helping developers discover GitHub repositories by learning from activity signals. It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings. -
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Judge0 IDE
Simple, free and open-source online code editor
...It's perfect for anybody who just wants to quickly write and run some code without opening a full-featured IDE on their computer. Moreover, it is also useful for teaching and learning or just trying out a new language. -
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Smart Algorithm
Repository implementing a variety of intelligent algorithms
...The implementations are provided in multiple languages (Java, Python, MATLAB). The repository’s aim is to offer reference implementations of “smart” algorithms for tasks like route planning, optimization, or algorithm learning. Particle Swarm Optimization (PSO) implementations in multiple languages. Immune Algorithm (or immune-inspired optimization) implementations. Multiple versions/language compatibility (Java, Python, MATLAB). -
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JS Bits
JavaScript concepts with code
JS Bits is an open-source collection of JavaScript concepts explained through concise code examples and clear explanations that serve as a practical learning resource for both beginners and experienced developers. It breaks down essential parts of the language — such as core syntax, quirky behaviors, functions, scope, closures, asynchronous patterns, and commonly misunderstood features — into digestible pieces that make it easier to internalize how JavaScript really works in modern development. ... -
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Spring Cloud Examples
Spring Cloud learning cases, service discovery, service governance
The Spring Cloud Examples repository appears to be a collection of sample applications and demos that illustrate how to use Spring Cloud and related cloud-native patterns in real-world microservice or distributed-service contexts. It provides working codebases showing how to wire together service discovery, configuration, inter-service communication, and possibly resilience patterns — giving developers a hands-on playground rather than theoretical documentation. By studying the examples, one... -
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MADDPG
Code for the MADDPG algorithm from a paper
MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in cooperative, competitive, and mixed settings. The code is built on top of TensorFlow and integrates with the Multiagent Particle Environments (MPE) for benchmarking. ... -
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The Neural Process Family
This repository contains notebook implementations
Neural Processes (NPs) is a collection of interactive Jupyter/Colab notebook implementations developed by Google DeepMind, showcasing three foundational probabilistic machine learning models: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). These models combine the strengths of neural networks and stochastic processes, allowing for flexible function approximation with uncertainty estimation. They can learn distributions over functions from data and efficiently make predictions at new inputs with calibrated uncertainty — making them useful for few-shot learning, Bayesian regression, and meta-learning. ... -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
...You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the theory of transfer learning and show how to apply it in useful projects. The development is on progress! The API will be updated soon, the more talented and light-weight API will be available in this repo! Detailed API documentation and sample jupyter notebooks that explain basic usages of API will be added! -
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Developer Roadmap Chinese
The roadmap to becoming a web developer in 2021
...You should have a better understanding of why a certain tool is more suitable for use in certain situations than others, and remember that the trend and popularity never mean that it is the most suitable tool for the task. These roadmaps cover all the learning content of the path below. Don't feel overwhelmed. If you are just getting started, you don't need to learn everything from the beginning. -
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Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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WebRTC-Experiment
WebRTC, WebRTC and WebRTC. Everything here is all about WebRTC
...Developers can study the examples to understand WebRTC APIs, signaling patterns, browser support, device detection, and peer-to-peer behavior. It is useful for prototyping browser-based communication apps and learning practical WebRTC implementation ideas. -
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IOV Weave
Easy-to-use SDK to build Tendermint ABCI applications
...More directly, it is based on the official cosmos-sdk, both the 0.8 release as well as the future 0.9 rewrite. Naturally, as I was the main author of 0.8. While both of those are extremely powerful and flexible and contain advanced features, they have a steep learning curve for novice users. Thus, this library aims to favor simplicity over power when there is a choice. -
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threadandjuc
High performance three-high-import import system
threadandjuc is a Java learning repository focused on multithreading, concurrency, and JUC concepts. It is designed to help developers understand how Java concurrent programming works through examples, explanations, and practical project-style demonstrations. The project covers topics such as threads, locks, synchronization, thread pools, concurrent collections, and high-performance data handling. -
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ChainerRL
ChainerRL is a deep reinforcement learning library
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. ... -
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CrypTen
A framework for Privacy Preserving Machine Learning
CrypTen is a research framework developed by Facebook Research for privacy-preserving machine learning built directly on top of PyTorch. It provides a secure and intuitive environment for performing computations on encrypted data using Secure Multiparty Computation (SMPC). Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. ... -
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deep2Read
This website includes a (growing) list of papers and lectures
As a group, we need to improve our knowledge of the fast-growing field of deep learning. To educate students in our graduate programs, to help new members in my team with basic tutorials, and to help current members understand advanced topics better, this website includes a (growing) list of tutorials and papers we survey for such a purpose. We hope this website helps people who share similar research interests or those interested in learning advanced topics about deep learning. -
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Baselines
High-quality implementations of reinforcement learning algorithms
...If you meant a different “baselines” (e.g. OpenAI Baselines for reinforcement learning), I can look up that specific one.