Search Results for "learning" - Page 82
Sort By:
Showing 4197 open source projects for "learning"
View related business solutions-
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
1
Acid Game Engine
A high speed C++17 Vulkan game engine
Acid is an open-source project listed on GitHub, but as of now its public presence is minimal (few stars, limited documentation), and there is little publicly available description of its scope or design intentions beyond repository contents. As such it appears to be a small-scale or early-stage engine or toolkit — perhaps experimental — rather than a mature, widely-adopted framework. Because of the lack of detailed docs, it’s hard to say exactly what kinds of games/applications it targets,... -
2
Linux-Tutorial
Learning repository written from the perspective of a Java programmer
Linux-Tutorial is a Simplified Chinese Linux learning repository written from the perspective of a Java programmer. It aims to help more developers enter the Linux world by organizing practical notes, installation guides, command references, and server setup material. The content covers Linux basics, Ubuntu, CentOS, Kali Linux, VMware, Vim, SSH, Bash, Sed, compression tools, Yum, Zsh, and many development operations topics. -
3
Deep learning time series forecasting
Deep learning PyTorch library for time series forecasting
Example image Flow Forecast (FF) is an open-source deep learning for time series forecasting framework. It provides all the latest state-of-the-art models (transformers, attention models, GRUs) and cutting-edge concepts with easy-to-understand interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to feature support for transformer-based models and remains the only true end-to-end deep learning for time series forecasting framework. ... -
4
MuZero General
A commented and documented implementation of MuZero
muzero-general is an open-source implementation of the MuZero reinforcement learning algorithm introduced by DeepMind. MuZero is a model-based reinforcement learning method that combines neural networks with Monte Carlo Tree Search to learn decision-making policies without requiring explicit knowledge of the environment’s dynamics. The repository provides a well-documented and commented implementation designed primarily for educational purposes. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
5
100+ Python Projects Challenge
100+ Python Projects Challenge
100+ Python Projects Challenge is a project-based Python learning repository focused on building many small applications and utilities. Instead of only practicing isolated syntax, it encourages learners to apply Python through complete mini-projects. The challenges span practical scripts, beginner applications, automation ideas, games, data tasks, and other hands-on exercises. This makes it useful for users who want to build a portfolio of small programs while reinforcing programming fundamentals. ... -
6
GPCS4
A work-in-progress PlayStation 4 emulator
A PlayStation 4 emulator which was just started. A project done for fun, and for technical research. The only reason I have started this project is for learning 3D graphics. It's not intended to become a serious application. Support simple compute shader, compute ring, cube maps. For more details you could join our discord server to contact me, or if you are used to Tencent QQ/TIM, just join our group. There you can get more resources which are useful for contributing. To run or develop GPCS4, a CPU supporting AVX instruction set as well as a graphics card supporting Vulkan 1.3 are required. -
7
Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
8
the-way-to-go_ZH_CN
The Chinese translation of "The Way to Go" Guide
the-way-to-go_ZH_CN is the Chinese translation of the Go programming book The Way to Go, known as (“Go Introduction Guide”). The translator, along with a team of contributors, created this project to make a high-quality Go learning resource accessible to Chinese readers who might struggle with English-language books. The README explains that the original author, Ivo Balbaert, granted authorization for the translation and even for updating certain content to match newer Go versions. The repository is structured like a full book: there are directories for cover and eBook materials, a table of contents file (TOC.md), configuration for building, and the chapters themselves in Markdown. ... -
9
ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
ModelFox makes it easy to train, deploy, and monitor machine learning models. Train a model from a CSV file on the command line. Make predictions from Elixir, Go, JavaScript, PHP, Python, Ruby, or Rust. Learn about your models and monitor them in production from your browser. ModelFox makes it easy to train, deploy, and monitor machine learning models. You can install the modelfox CLI by either downloading the binary from the latest GitHub release or by building from source. ... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
10
SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
...The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. -
11
paip-lisp
Lisp code for the textbook "Paradigms of Artificial Intelligence"
...It is valuable for readers who want to study the original code while working through the text or revisiting older AI ideas. The code also serves as a historical reference for how AI programming was taught before today’s deep-learning-centered ecosystem. paip-lisp is best suited for learners interested in Lisp, symbolic AI, and the foundations of practical AI programming. -
12
starboard-notebook
In-browser literate notebooks
...Starboard notebooks support multiple languages, and the tooling emphasizes portability, making notebooks viewable and executable even on mobile devices or static sites. Because the runtime runs entirely inside the browser, it removes installation barriers and provides a sandboxed environment for exploration, learning, and sharing. The project plays into a larger ecosystem of in-browser computation and literate programming tools, helping users publish interactive content on the web with minimal setup. -
13
pyTorch Tutorials
Build your neural network easy and fast
...The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks, recurrent networks, autoencoders, and generative adversarial networks, which gives learners practical exposure to real machine learning tasks. Each example explains PyTorch’s dynamic computation graph, optimization techniques, and core abstractions in a way that is accessible and reproducible. Contributors and authors integrate visual and coded examples so readers can see both the theory and the implementation side-by-side. -
14
pyprobml
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Python 3 code to reproduce the figures in the books Probabilistic Machine Learning: An Introduction (aka "book 1") and Probabilistic Machine Learning: Advanced Topics (aka "book 2"). The code uses the standard Python libraries, such as numpy, scipy, matplotlib, sklearn, etc. Some of the code (especially in book 2) also uses JAX, and in some parts of book 1, we also use Tensorflow 2 and a little bit of Torch. See also probml-utils for some utility code that is shared across multiple notebooks. -
15
Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. ... -
16
ml5.js
Friendly machine learning for the web
A neighborly approach to creating and exploring artificial intelligence in the browser. ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies. -
17
ASRT Speech Recognition
A Deep-Learning-Based Chinese Speech Recognition System
ASRT is an end-to-end deep-learning Chinese ASR system built with TensorFlow/Keras, using convolution + CTC and a Max-Entropy HMM language model. It provides a REST/gRPC server backend and client SDKs in multiple languages (Python, Java, Go, Windows). Notably lightweight, it performs well without needing GPU acceleration and runs across platforms, targeting developers and researchers building Chinese voice interfaces. -
18
CoolplaySpark
Spark Cool Play: Spark source code analysis, Spark class library, etc.
CoolplaySpark is a learning and practice repository designed to help users understand and work with Apache Spark. It serves as a companion resource for the book 深入理解Spark核心思想与源码分析 (In-Depth Understanding of Spark’s Core Concepts and Source Code Analysis). The project contains annotated examples, explanations, and exercises that guide learners through Spark’s architecture, execution model, and source code internals. -
19
FibroSoft
Histology staining analysis and quantification tool.
A histology staining analysis and quantification tool based on machine learning. -
20
DeepLearning Tutorial
Deep Learning Tutorial, Excellent Articles, Deep Learning Tutorial
DeepLearning is an open-source repository that aggregates tutorials, articles, and educational resources related to deep learning and machine learning. The project is designed as a knowledge collection that helps beginners understand neural networks, deep learning architectures, and fundamental machine learning concepts. It contains curated learning materials covering topics such as feedforward neural networks, activation functions, backpropagation algorithms, optimization methods, and convolutional neural networks. ... -
21
http-server
A simple zero-configuration command-line http server
http-server is a simple, zero-configuration command-line static HTTP server. It is powerful enough for production usage, but it's simple and hackable enough to be used for testing, local development and learning. You will be prompted with a few questions after entering the command. Use 127.0.0.1 as value for Common name if you want to be able to install the certificate in your OS's root certificate store or browser so that it is trusted. If you wish to use a passphrase with your private key you can include one in the openssl command via the -passout parameter (using password of foobar) -
22
Spring MicroServices
Microservices using Spring Boot, Spring Cloud, Docker and Kubernetes
Spring Microservices is a practical learning repository focused on building microservices architectures using Spring Boot and Spring Cloud. It teaches developers how to design RESTful APIs and evolve them into scalable microservices systems. The project includes examples of service communication, load balancing, and centralized configuration. It also introduces tools such as Eureka, Zipkin, and API gateways for managing distributed systems. -
23
Guild AI
Experiment tracking, ML developer tools
Guild AI is an open-source experiment tracking toolkit designed to bring systematic control to machine learning workflows, enabling users to build better models faster. It automatically captures every detail of training runs as unique experiments, facilitating comprehensive tracking and analysis. Users can compare and analyze runs to deepen their understanding and incrementally improve models. Guild AI simplifies hyperparameter tuning by applying state-of-the-art algorithms through straightforward commands, eliminating the need for complex trial setups. ... -
24
OpenPrompt
An Open-Source Framework for Prompt-Learning
Prompt-learning is the latest paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modifies the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. OpenPrompt is a library built upon PyTorch and provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. -
25
Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable.