Search Results for "learning" - Page 92
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Showing 4819 open source projects for "learning"
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Music Source Separation
Separate audio recordings into individual sources
...The repository provides training scripts (e.g. using datasets such as MUSDB18), preprocessing steps (audio-to-HDF5 packing, indexing), evaluation pipelines, and inference scripts to perform separation on arbitrary audio files. This makes the project useful both for researchers in music information retrieval / audio machine learning and for hobbyists or practitioners who want to experiment with remixing, karaoke, or audio editing. -
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Awesome Decision Tree Papers
A collection of research papers on decision, classification, etc.
A collection of research papers on decision, classification and regression trees with implementations. -
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LenMus
LenMus is a free program for learning music theory.
*** Development moved to GitHub at https://github.com/lenmus/lenmus *** LenMus is a free program for learning music. It allows you to focus on specific skills and exercises, on both theory and aural training. The different activities can be customized to meet your needs. It includes an score editor. -
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codeforces-go
Solutions to Codeforces by Go
Golang algorithm competition template library. Due to the complexity of algorithm knowledge points, it is necessary to classify the algorithms you have learned and the questions you have done. An algorithm template should cover the following points. Basic introduction to the algorithm (core idea, complexity, etc.) Reference links or book chapters (good material) Template code (can contain some comments, usage instructions) Template supplements (extra codes in common question types, modeling... -
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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vue2-happyfri
Learning-oriented app that recreates a mobile food-ordering experience
vue2-happyfri is a learning-oriented Vue 2 application that recreates a mobile food-ordering experience to demonstrate how to structure a real-world single-page app. It shows how to compose UI from reusable components, wire up routing between views, and manage shared state for carts, users, and menus. The project emphasizes responsive, touch-friendly interactions and small polish details—like transitions, toasts, and loading states—that make the app feel production-ready. -
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SVoice (Speech Voice Separation)
We provide a PyTorch implementation of the paper Voice Separation
SVoice is a PyTorch-based implementation of Facebook Research’s study on speaker voice separation as described in the paper “Voice Separation with an Unknown Number of Multiple Speakers.” This project presents a deep learning framework capable of separating mixed audio sequences where several people speak simultaneously, without prior knowledge of how many speakers are present. The model employs gated neural networks with recurrent processing blocks that disentangle voices over multiple computational steps, while maintaining speaker consistency across output channels. ... -
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Deep Learning 500 Questions
500 Questions on Deep Learning using a question-and-answer format
DeepLearning-500-questions is a comprehensive handbook that compiles 500 important questions on deep learning, curated to serve as a valuable reference for AI engineer interviews and self-study. Edited by Tan Jiyong with contributions from Guo Zizhao, Li Jian, and Dian Songyi, the book systematically covers both theoretical foundations and practical applications of deep learning. The first sections focus on essential mathematics, machine learning basics, and deep learning foundations, establishing the groundwork for more advanced topics. ... -
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
VQGAN-CLIP has been in vogue for generating art using deep learning. Searching the r/deepdream subreddit for VQGAN-CLIP yields quite a number of results. Basically, VQGAN can generate pretty high-fidelity images, while CLIP can produce relevant captions for images. Combined, VQGAN-CLIP can take prompts from human input, and iterate to generate images that fit the prompts. Thanks to the generosity of creators sharing notebooks on Google Colab, the VQGAN-CLIP technique has seen widespread circulation. ... -
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Record matching software
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ReinforcementLearningAnIntroduction.jl
Julia code for the book Reinforcement Learning An Introduction
This project provides the Julia code to generate figures in the book Reinforcement Learning: An Introduction(2nd). One of our main goals is to help users understand the basic concepts of reinforcement learning from an engineer's perspective. Once you have grasped how different components are organized, you're ready to explore a wide variety of modern deep reinforcement learning algorithms in ReinforcementLearningZoo.jl. -
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node-elm
Backend system based on node.js + Mongodb
node-elm is a Node.js back end that powers the data side of the Ele.me clone ecosystem, complementing the Vue front-end projects. It exposes RESTful endpoints for entities such as restaurants, menus, orders, users, and addresses to simulate a realistic delivery stack. The server demonstrates how to structure a project with routing, controllers, and middleware to keep logic testable and readable. It typically includes authentication flows, input validation, and error handling that mirror... -
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scikit-learn tips
50 scikit-learn tips
scikit-learn-tips is an educational repository that collects practical advice and best practices for using the scikit-learn machine learning library effectively. The project consists of short explanations and examples that highlight common patterns, pitfalls, and techniques used when building machine learning workflows in Python. Each tip typically demonstrates how specific components of scikit-learn, such as pipelines, preprocessing utilities, or model evaluation tools, should be applied in real projects. ... -
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Data Science and Machine Learning Career
Career Resources for Data Science, Machine Learning and Big Data
Data Science and Machine Learning Career is a curated career resource for people pursuing analytical and technical roles. It gathers job platforms, employer review sites, respected courses, competitions, tools, skill areas, and bootcamps in one place. The repository also organizes podcasts for different experience levels and lists common career paths across data science, analytics, engineering, and business intelligence. -
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Trax
Deep learning with clear code and speed
Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively used and maintained in the Google Brain team. Run a pre-trained Transformer, create a translator in a few lines of code. Features and resources, API docs, where to talk to us, how to open an issue and more. Walkthrough, how Trax works, how to make new models and train on your own data. -
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Learn Space PC (Version 2.0)
An app to learn about space.
Learn Space PC (LSFU) is a simple app for learning about space and the heavenly bodies. Its open-source, code available at - https://github.com/UnknownCoder56/LSFU Currently under active development. Check out for new releases! -
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Reformer PyTorch
Reformer, the efficient Transformer, in Pytorch
This is a Pytorch implementation of Reformer. It includes LSH attention, reversible network, and chunking. It has been validated with an auto-regressive task (enwik8). -
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Nesta área você encontra os códigos dos algoritmos dos exemplos de aprendizagem tratados no livro "Algoritmos: Lógica para desenvolvimento de programação de computadores" codificados nos interpretadores algorítmicos VisuAlg, ILA+, G-Portugol, Portugol Online, Portugol Studio e Portugol IDE. In this area you will find the codes of the algorithms of the learning examples dealt with in the book "Algorithms: Logic for computer programming development" coded in the VisuAlg, ILA+, G-Portugol, Portugol Online, Portugol Studio and Portugol IDE algorithmic interpreters.
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Machine Learning course
Open Machine Learning course
The first semester of the giraffe-ai Machine Learning course. Open Machine Learning course. -
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hora
Efficient approximate nearest neighbor search algorithm collections
hora is an open-source high-performance vector similarity search library designed for large-scale machine learning and information retrieval systems. The project focuses on approximate nearest neighbor search, a fundamental technique used in modern AI applications such as recommendation systems, image search, and semantic search engines. Hora implements multiple efficient indexing algorithms that allow systems to rapidly search through high-dimensional vectors produced by machine learning models. ... -
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Ferret
Ferret is a web scraping system
Ferret is a web scraping system. It aims to simplify data extraction from the web for UI testing, machine learning, analytics and more. ferret allows users to focus on the data. It abstracts away the technical details and complexity of underlying technologies using its own declarative language. It is extremely portable, extensible, and fast. -
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YOLOv4
PyTorch implementation of YOLOv4
...The repository supports common detection workflows such as dataset preparation, model training, evaluation, inference, and weight conversion. It is useful for developers who prefer the PyTorch ecosystem for experimentation, debugging, and integration with other machine learning tooling. The project also connects to the broader YOLOv4 family, including CSP-based architecture ideas and real-time detection improvements. It is best suited for researchers and engineers who want YOLOv4 behavior in a Python-first deep learning environment. -
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TensorFlow Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
TensorFlow Examples is a comprehensive repository of example implementations, tutorials, and reference code intended to help newcomers and intermediate learners dive into TensorFlow quickly. It contains both Jupyter notebooks and raw source code, covering a broad range of tasks: from basic machine-learning and neural-network models to more advanced use cases, using both TensorFlow v1 and v2 APIs. For clarity and educational value, each example is accompanied by explanatory comments or markdown cells to illustrate what the code does and why — a design that makes it especially suitable for self-learners or students following along with real data. ... -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. ... -
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Advanced CSS Course
Starter files, final projects and FAQ for my Advanced CSS course
...The repository focuses on modern CSS techniques such as Sass, responsive layouts, animations, Flexbox, CSS Grid, and reusable design patterns. It also includes downloadable course slides, making it easier to follow the theory portions alongside the hands-on projects. The code is meant for learning rather than production reuse, but it provides clear examples of how advanced CSS concepts are applied in real interfaces. It is best suited for students who already know basic HTML and CSS and want to move into polished, professional frontend styling. -
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Scikit-Optimize
Sequential model-based optimization with a `scipy.optimize` interface
Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts. The library is built on top of NumPy, SciPy and Scikit-Learn.