Search Results for "learning" - Page 95
Sort By:
Showing 4736 open source projects for "learning"
View related business solutions-
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.
-
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.
-
1
Machine-Learning-Notes
Zhou Zhihua's "Machine Learning" push notes
The Machine-Learning-Notes repository contains detailed handwritten-style study notes based on the popular machine learning textbook by Zhou Zhihua. The project focuses on deriving formulas and explaining algorithms step by step so that learners can understand the mathematical foundations behind machine learning methods. The notes span sixteen chapters that cover a wide range of topics, including model evaluation, linear models, decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimensionality reduction, and reinforcement learning. -
2
Transformer TTS
Implementation of a Transformer based neural network
...It takes inspiration from architectures like FastSpeech, FastSpeech 2, FastPitch, and Transformer TTS, and extends them with its own aligner and forward models. The system separates alignment learning and acoustic modeling: an autoregressive Transformer is used as an aligner to extract phoneme-to-frame durations, while a non-autoregressive “ForwardTransformer” generates mel-spectrograms conditioned on text and durations. This design addresses common autoregressive issues such as repetition, skipped words, and unstable attention, and results in robust, fast synthesis where all frames are predicted in parallel. ... -
3
vim-bootstrap
A simple method of generating a configuration for vim / neovim
...Vim is often called a "programmer's editor," and is so useful for programming that many consider it an entire IDE. Put your focus on one thing at a time, when we focus on studying a single thing we accelerate our learning. Created and maintained by a community that uses the Neovim/Vim on a daily basis. -
4
jiant
jiant is an nlp toolkit
Jiant is a multitask NLP framework for fine-tuning transformer-based models on multiple natural language understanding (NLU) tasks. -
$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.
-
5
TNN
Uniform deep learning inference framework for mobile
TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. At the same time, it refers to the high performance and good scalability characteristics of the industry's... -
6
Opyrator
Turns your machine learning code into microservices with web API
Instantly turn your Python functions into production-ready microservices. Deploy and access your services via HTTP API or interactive UI. Seamlessly export your services into portable, shareable, and executable files or Docker images. Opyrator builds on open standards - OpenAPI, JSON Schema, and Python type hints - and is powered by FastAPI, Streamlit, and Pydantic. It cuts out all the pain for productizing and sharing your Python code - or anything you can wrap into a single Python... -
7
Chess.jl
Julia chess programming library
...There are functions for creating and manipulating chess games, chess positions and sets of squares on the board, for reading and writing chess games in the popular PGN format (including support for comments and variations), for creating opening trees, and for interacting with UCI chess engines. The library was designed for the purpose of doing machine learning experiments in computer chess, but it should also be suitable for most other types of chess software. -
8
EEPROM_editor
EEPROM programmer for the SST39SF040 chip.
...This fills the gap between circuits that are too complex for discrete logic gates while also not being suitable for Arduino type control. FPGAs are suitable for these applications, but the learning curve is steep, the tools are complex and for small one off projects, not worth the effort. An Arduino Nano will be used as an interposer between a Python script doing the programming and SPI connected port expander chips used for generating the required signals. This makes project development simple with relatively simple tools, simple hardware and a shallow learning curve. ... -
9
raspberry-pi-os
Learning operating system development using Linux kernel and Raspberry
...The repository is structured so that every lesson corresponds to a snapshot of the source code at that stage, making it easier for learners to follow the progression of kernel development. The design of the lessons emphasizes gradual learning, allowing readers without prior OS development experience to understand kernel internals. The project is still under active development and encourages community contributions to expand its lessons and improve its content. -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
MLDataUtils.jl
Utility package for generating, loading, and processing ML datasets
This package is designed to be the end-user facing front-end to all the data related functionality that is spread out across the JuliaML ecosystem. Most of the following sub-categories are covered by a single back-end package that is specialized on that specific problem. Consequently, if one of the following topics is of special interest to you, make sure to check out the corresponding documentation of that package. -
11
DeepSpeech
Open source embedded speech-to-text engine
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following the instructions in the usage docs. If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the DeepSpeech releases page. -
12
Couler
Unified Interface for Constructing and Managing Workflows
Couler is a system designed for unified machine learning workflow optimization in the cloud. Couler endeavors to provide a unified interface for constructing and optimizing workflows across various workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow. Couler enhances workflow efficiency through features like Autonomous Workflow Construction, Automatic Artifact Caching Mechanisms, Big Workflow Auto Parallelism Optimization, and Automatic Hyperparameters Tuning. -
13
Stats With Julia Book
Collection of runnable Julia code examples for a statistics book
StatsWithJuliaBook is the companion code repository for the book Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. It contains over 200 code blocks that correspond to the book’s ten chapters and three appendices, covering topics from probability theory and data summarization to regression analysis, hypothesis testing, and machine learning basics. The repository is designed for Julia users and provides ready-to-run examples that reinforce theoretical concepts with practical implementation. ... -
14
MLOps Course
Learn how to design, develop, deploy and iterate on ML apps
The MLOps Course by Goku Mohandas is an open-source curriculum that teaches how to combine machine learning with solid software engineering to build production-grade ML applications. It is structured around the full lifecycle: data pipelines, modeling, experiment tracking, deployment, testing, monitoring, and iteration. The repository itself contains configuration, code examples, and links to accompanying lessons hosted on the Made With ML site, which provide detailed narrative explanations and diagrams. ... -
15
Semantic Segmentation in PyTorch
Semantic segmentation models, datasets & losses implemented in PyTorch
...PyTorch and Torchvision needs to be installed before running the scripts, together with PIL and opencv for data-preprocessing and tqdm for showing the training progress. PyTorch v1.1 is supported (using the new supported tensoboard); can work with earlier versions, but instead of using tensoboard, use tensoboardX. Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero during training. Considered as the go-to scheduler for semantic segmentation. One Cycle learning rate, for a learning rate LR, we start from LR / 10 up to LR for 30% of the training time, and we scale down to LR / 25 for remaining time, the scaling is done in a cos annealing fashion (see Figure bellow), the momentum is also modified but in the opposite manner starting from 0.95 down to 0.85 and up to 0.95. -
16
CapsGNN
A PyTorch implementation of "Capsule Graph Neural Network"
A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph... -
17
YOLO ROS
YOLO ROS: Real-Time Object Detection for ROS
This is a ROS package developed for object detection in camera images. You only look once (YOLO) is a state-of-the-art, real-time object detection system. In the following ROS package, you are able to use YOLO (V3) on GPU and CPU. The pre-trained model of the convolutional neural network is able to detect pre-trained classes including the data set from VOC and COCO, or you can also create a network with your own detection objects. The YOLO packages have been tested under ROS Noetic and... -
18
Lucid
A collection of infrastructure and tools for research
Lucid is a collection of infrastructure and tools for research in neural network interpretability. Lucid is research code, not production code. We provide no guarantee it will work for your use case. Lucid is maintained by volunteers who are unable to provide significant technical support. Start visualizing neural networks with no setup. The following notebooks run right from your browser, thanks to Collaboratory. It's a Jupyter notebook environment that requires no setup to use and runs... -
19
Keepsake
Version control for machine learning
Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook. -
20
Gluon CV Toolkit
Gluon CV Toolkit
...The model zoo is the one-stop shopping center for many models you are expecting. GluonCV embraces a flexible development pattern while is super easy to optimize and deploy without retaining a heavyweight deep learning framework. -
21
gym-pybullet-drones
PyBullet Gymnasium environments for multi-agent reinforcement
Gym-PyBullet-Drones is an open-source Gym-compatible environment for training and evaluating reinforcement learning agents on drone control and swarm robotics tasks. It leverages the PyBullet physics engine to simulate quadrotors and provides a platform for studying control, navigation, and coordination of single and multiple drones in 3D space. -
22
BitTiger
Lifelong Learning University from Silicon Valley
BitTiger is an extensive open educational repository that functions as a self-guided curriculum covering a wide range of topics in computer science, artificial intelligence, blockchain, system design, and technical interview preparation. Rather than being a traditional software application, it is structured as a knowledge base composed of curated learning materials, tutorials, and practical case studies designed to simulate real-world problem solving. The project reflects the philosophy of a “lifelong learning university,” aiming to help learners bridge the gap between academic knowledge and industry skills by exploring applied concepts across multiple domains. It includes structured modules on algorithms, system design, and emerging technologies, along with hands-on examples such as blockchain implementations and AI experiments. -
23
Swift for TensorFlow
Swift for TensorFlow
Swift for TensorFlow repository contains the open-source implementation of Swift for TensorFlow, a project that integrates machine learning capabilities directly into the Swift programming language. The initiative aims to provide a new programming model for developing machine learning systems by combining the power of TensorFlow with language-level features such as automatic differentiation and strong type systems. By embedding machine learning functionality into the Swift compiler and language design, the project enables developers to write high-performance machine learning models while maintaining the readability and safety of modern programming practices. ... -
24
Brain AutoML
Google Brain AutoML
This repository contains a list of AutoML related models and libraries. Google Brain researchers have introduced a new way of programming automated machine learning (AutoML) based on symbolic programming. The researchers also proposed PyGlove, a general symbolic programming library for Python, to implement the symbolic formulation of AutoML. AutoML, designed to fill the machine learning industry’s talent gap, is gaining traction among various organizations. -
25
earthengine-py-notebooks
A collection of 360+ Jupyter Python notebook examples
earthengine-py-notebooks is a comprehensive collection of hundreds of Jupyter Python notebooks that serve as examples and tutorials for using the Google Earth Engine Python API. These notebooks are organized into thematic areas such as image processing, machine learning, visualization, filtering, and asset management, exposing users to real geospatial analysis tasks. The repository makes it easier to explore Earth Engine’s large geospatial data catalog, interactively display map layers, and generate visual insights without the need for external GIS software by leveraging interactive widgets and mapping libraries. ...