Search Results for "learning" - Page 66
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Showing 4736 open source projects for "learning"
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Just download the above zip file, extract it and then open the index.html file on internet browsers like Firefox ( preferable ) or Google Chrome.
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DeepChem
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, etc
...The DeepChem project maintains an extensive collection of tutorials. All tutorials are designed to be run on Google collab (or locally if you prefer). Tutorials are arranged in a suggested learning sequence that will take you from beginner to proficient at molecular machine learning and computational biology more broadly. -
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OneFile
Curates delightful open-source projects
OneFile curates delightful open-source projects that each consist of a single file, highlighting how much you can learn from compact, self-contained code. The collection spans tiny games, utilities, servers, interpreters, and teaching demos where the entire idea fits in one readable source. This “one file” constraint makes every example approachable—you can open it, grasp the full control flow, and experiment without navigating a large codebase. It’s ideal for learners seeking quick wins or... -
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Premium Markets
Premium Markets is an automated financial technical analysis system.
...Full installation instructions can be found at http://www.premiummarkets.uk/html/swtui.html#Download . I would also like to bring to your attention that, in its advanced version, Premium Markets also provides a Forecast machine learning engine based on neural networks. See http://www.premiummarkets.uk/ for documentation and a workable demo of the trend forecast and prediction engine. -
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Data Annotator for Machine Learning
Data annotator for machine learning
Data annotator for machine learning allows you to centrally create, manage and administer annotation projects for machine learning. Data Annotator for Machine Learning (DAML) is an application that helps machine learning teams facilitate the creation and management of annotations. Active learning with uncertain sampling to query unlabeled data. Project tracking with real-time data aggregation and review process. -
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SMLX
A sheet music language, easy and intuitive to use
The purpose of SMLX project is to devise a sheet music language that most digital devices with keypad (e.g. mobile phones) can type melodies easily and intuitively for communication. From this basic, SMLX is capable of being a language for developing music. In other words, one can compose or modify music (multi-channel) by using vi editor. SMLX 計劃的主要目的是開發一種樂譜記載方式,以方便數位樂譜記錄及傳訊交流的目的. 對於音樂為主的使用者而言,使用一般vi編輯器即可編修多軌樂譜: .方便輸入修改: 一般傳(簡)訊時可簡易使用鍵盤表達旋律. 一般所見到的樂譜都可 ... -
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JQM Java Quine McCluskey
JQM - Java Quine McCluskey for minimization of Boolean functions.
Java Quine McCluskey (JQM) implements the Quine-McCluskey algorithm with Petrick’s Method for minimizing Boolean functions. Designed for both education and industrial application, it handles up to 16 variables and functions. Uniquely, JQM bridges the gap between theory and practice: it visualizes the solution process with generated Karnaugh Maps for students, while supporting PLC engineers by exporting results to Structured Text (ST) and Ladder Diagram (LD). The software includes a GUI for... -
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Syslogd2
High capacity syslog data collection, filtering, and management.
Syslogd2 is a syslog daemon that has been completely re-imagined specifically for use in network environments. It is multi-threaded, scalable and versatile with features designed for both network and host managers. Each Syslogd2 binary is customized from a set of over 20 features at compile-time. It can support input from text files, named-pipes, Linux kernel and user-defined Linux and (both IPv4 and IPv6) IP sockets (both UDP and TCP). It provides a pre-loadable name-cache that can... -
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Yangshun Lago
Data Structures and Algorithms library in TypeScript and JavaScript
Lago is a study-oriented library of classic data structures and algorithms implemented in JavaScript with an emphasis on readability and learning. Instead of aiming to be a production runtime, it serves as a reference you can step through to understand how arrays, stacks, queues, linked lists, trees, heaps, graphs, and sorting/searching routines actually work. The implementations favor clarity over micro-optimizations, making them approachable for learners who are new to algorithmic thinking or coming from non-CS backgrounds. ... -
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minbpe
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm
...It operates on UTF-8 encoded bytes rather than Unicode characters, which makes it robust to arbitrary text inputs and avoids needing a language-specific character vocabulary. The repository is structured as a teaching-oriented implementation that shows how to train a tokenizer by learning merge rules, then apply those merges to encode text into token IDs and decode tokens back into text. It is intentionally small and readable so developers can understand each stage of BPE, including the mechanics of pair counting, merge application, and vocabulary growth. The project is especially useful for practitioners who want to demystify how LLM tokenizers work or who need a lightweight reference implementation for experimentation. -
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A Survey of Surveys
A collection of 1000+ survey papers on Natural Language Processing
...These topics include areas such as neural machine translation, language models, computer vision, and deep learning architectures. The repository organizes hundreds of papers into thematic categories and includes references, links, and bibliographic information to facilitate research and literature exploration. -
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Encord Active
The toolkit to test, validate, and evaluate your models and surface
Encord Active is an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance. Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically. Discover errors, outliers, and edge-cases within your data - all in one open source... -
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higgsfield
Fault-tolerant, highly scalable GPU orchestration
Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs). -
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spider_collection
Collection of Python web scraping scripts for data extraction tasks
spider_collection is a collection of Python web crawler scripts created primarily for experimentation, learning, and practical scraping tasks. spider_collection gathers multiple independent spiders designed to collect data from different platforms and services, demonstrating a variety of scraping techniques and workflows. These crawlers make use of common Python scraping tools such as requests, parsel, BeautifulSoup, and the Scrapy framework to extract structured information from web pages. ... -
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Flutter Vignettes
A collection of fun Flutter experiments, created by gskinner
...It’s not only a code resource but also a design showcase, blending engineering and artistry. By presenting small, focused experiments, flutter_vignettes encourages experimentation and learning while illustrating Flutter’s strengths in rapid UI prototyping. -
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mall-admin-web
frontend component of the “mall” e-commerce system
mall-admin-web is the frontend component of the “mall” e-commerce system, implemented in Vue (plus Element UI) to deliver the backend management interface: product management, order management, member management, promotions, operations, content, statistics and settings. It demonstrates how a modern single-page application can be structured to integrate with the backend services described in the learning / microservice projects, including authentication (JWT or token), REST APIs, role/permission management and real-time dashboards. The codebase offers modular UI architecture, Vue component patterns, dynamic menu and permission handling, and visualization of statistics and reports. For teams building an admin portal or internal operations system, this project provides a concrete blueprint for UI design, API integration, state management and role-based access control. ... -
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Zeta
Build high-performance AI models with modular building blocks
zeta is a deep learning library focused on providing cutting-edge AI and neural network models with a strong emphasis on research-grade architectures. It includes state-of-the-art implementations for rapid experimentation and model building. -
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applied-ml
Papers & tech blogs by companies sharing their work on data science
The applied-ml repository is a rich, curated collection of papers, technical articles, and case-study blog posts about how machine learning (ML) and data-driven systems are applied in real production environments by major companies. Instead of focusing solely on theoretical ML research, this repo highlights industry-scale challenges: data collection, quality, infrastructure, feature stores, model serving, monitoring, scalability, and how ML is embedded in product workflows. -
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Hasktorch
Tensors and neural networks in Haskell
Hasktorch is a powerful Haskell library for tensor computation and neural network modeling, built on top of libtorch (the backend of PyTorch). It brings differentiable programming, automatic differentiation, and efficient tensor operations into Haskell’s strongly typed functional paradigm. This project is in active development, so expect changes to the library API as it evolves. We would like to invite new users to join our Hasktorch discord space for questions and discussions.... -
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torchtext
Data loaders and abstractions for text and NLP
We recommend Anaconda as a Python package management system. Please refer to pytorch.org for the details of PyTorch installation. LTS versions are distributed through a different channel than the other versioned releases. Alternatively, you might want to use the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses. To build torchtext from source, you need git, CMake and C++11 compiler such as g++. When building from source, make sure that you have the same C++... -
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Transformers in Time Series
A professionally curated list of awesome resources
Transformers in Time Series is a curated research repository that collects academic papers, code implementations, datasets, and learning resources related to transformer models for time series analysis. The project was created to systematically organize the rapidly growing research field that applies transformer architectures to time series modeling tasks. It compiles literature from major conferences and journals and categorizes them by application domains such as forecasting, anomaly detection, and classification. ... -
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Useful Scripts
Useful scripts for making developer's everyday life easier
Useful scripts for making developers' everyday lives easier and happier, involving java, shell, etc. Usually useful manual operations are made into scripts for convenient use, making the daily life of development easier. Share the functions (i.e. requirements, ideas) that are commonly used but not written into scripts, and submit an Issue. The scripts of this warehouse (such as related scripts) are deployed and used in the online production environment of JavaAlibaba and other companies... -
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snntorch
Deep and online learning with spiking neural networks in Python
snntorch is a deep learning library that enables researchers and developers to build and train spiking neural networks using the PyTorch framework. Spiking neural networks are biologically inspired models that communicate through discrete spike events rather than continuous activation values, making them closer to how neurons operate in the brain. The library extends PyTorch’s tensor computation capabilities to support gradient-based learning for networks composed of spiking neurons. ... -
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Tensor Puzzles
Solve puzzles. Improve your pytorch
Tensor Puzzles is an interactive collection of 21 exercises for learning tensor programming in PyTorch and NumPy. Each puzzle asks the learner to recreate a familiar array operation from first principles. Solutions must fit on one short line and use only a restricted set of indexing, arithmetic, comparison, and broadcasting tools. Standard convenience functions such as sum, view, squeeze, and take are intentionally prohibited. -
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Deep Learning Models
A collection of various deep learning architectures, models, and tips
This repository collects clear, well-documented implementations of deep learning models and training utilities written by Sebastian Raschka. The code favors readability and pedagogy: components are organized so you can trace data flow through layers, losses, optimizers, and evaluation. Examples span fundamental architectures—MLPs, CNNs, RNN/Transformers—and practical tasks like image classification or text modeling.