Search Results for "learning language" - Page 9
Sort By:
Showing 810 open source projects for "learning language"
View related business solutions-
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
-
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
-
1
Label Sleuth
Open source no-code system for text annotation and building of text
...From task definition to working model in just a few hours! While domain experts label their data, Label Sleuth automatically trains in the background-appropriate machine learning models. To avoid wasted labeling effort, Label Sleuth employs active learning techniques to guide the user in what they should be labeled next. Domain experts can quickly start labeling their data through an intuitive user interface. Developed by researchers across industry and academia, Label Sleuth incorporates the latest research from human-computer interaction, natural language processing, and artificial intelligence. ... -
2
Zero to Mastery Deep Learning TensorFlow
All course materials for the Zero to Mastery Deep Learning with TF
...The material covers core machine learning workflows including regression, classification, computer vision, natural language processing, and time series forecasting, allowing users to build a well-rounded understanding of modern AI tasks. It also integrates milestone projects that simulate real-world scenarios, helping users translate abstract concepts into deployable solutions. -
3
Artificial Intelligence for Beginners
12 Weeks, 24 Lessons, AI for All
...The repository provides a 12-week program composed of 24 lessons that combine theory, code examples, quizzes, and laboratory exercises. It covers a broad range of topics including neural networks, computer vision, natural language processing, and AI ethics. The curriculum is intentionally beginner-friendly while still exposing learners to widely used frameworks such as TensorFlow and PyTorch. It also supports many languages, making the material accessible to a global audience. Overall, the project functions as a complete self-paced learning pathway for students, educators, and developers who want a practical introduction to modern AI concepts. -
4
Hello JavaScript
Course repository designed to teach JavaScript from scratch in Spanish
hello-javascript is a comprehensive course repository designed to teach JavaScript from scratch in Spanish, pairing source code with hours of video content and hundreds of exercises. It covers the JavaScript language core, from variables and control flow to functions, objects, arrays, and modern ECMAScript features. The project also touches the browser and Node.js ecosystems so learners can understand both client and server contexts. Exercises and lesson organization make it easy to follow along, practice, and review concepts as you go. The repository is openly licensed and structured so you can clone it, experiment locally, and track your learning progress. ... -
Secure File Transfer for Windows with Cerberus by RedwoodCerberus supports unlimited users and connections on a single IP, with built-in encryption, 2FA, and a browser-based web client — all deployable in under 15 minutes with a 25-day free trial.
-
5
Docspell
Assist in organizing your piles of documents
Docspell is a personal document organizer. Or sometimes called a "Document Management System" (DMS). You'll need a scanner to convert your papers into files. Docspell can then assist in organizing the resulting mess. It can unify your files from scanners, emails, and other sources. It is targeted for home use, i.e. families, households, and also for smaller groups/companies. You can associate tags, set correspondent,s and lots of other predefined and custom metadata. If your documents are... -
6
ModelScope
Bring the notion of Model-as-a-Service to life
ModelScope is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. In particular, with rich layers of API abstraction, the ModelScope library offers... -
7
Must-Watch JavaScript
JavaScript talks you have to see on functional programming
Must-Watch JavaScript is a curated list of high-quality talk videos, tutorials, and presentations about JavaScript — covering a variety of topics such as language features, best practices, frameworks, performance, tooling, and evolving JS ecosystem trends. Rather than being code-heavy, it serves as a learning resource for developers who want to gain conceptual wisdom, stay updated with JavaScript evolution, or learn from experts’ experiences and opinions. For someone who wants to deepen their understanding of JavaScript beyond “just writing code,” this repo offers a guided path through well-chosen content that can expand perspective: on architecture, patterns, pitfalls, performance tradeoffs, and the rationale behind design decisions in JS. ... -
8
Deep Lake
Data Lake for Deep Learning. Build, manage, and query datasets
Deep Lake (formerly known as Activeloop Hub) is a data lake for deep learning applications. Our open-source dataset format is optimized for rapid streaming and querying of data while training models at scale, and it includes a simple API for creating, storing, and collaborating on AI datasets of any size. It can be deployed locally or in the cloud, and it enables you to store all of your data in one place, ranging from simple annotations to large videos. Deep Lake is used by Google, Waymo,... -
9
spacy-transformers
Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
spaCy supports a number of transfer and multi-task learning workflows that can often help improve your pipeline’s efficiency or accuracy. Transfer learning refers to techniques such as word vector tables and language model pretraining. These techniques can be used to import knowledge from raw text into your pipeline, so that your models are able to generalize better from your annotated examples. -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
10
Lesan
New way to create web server and NoSQL data model
Lesan is a multilingual text processing and translation library designed for natural language processing (NLP) applications. It provides tools for text normalization, tokenization, and translation across multiple languages. -
11
LightAutoML
Fast and customizable framework for automatic ML model creation
LightAutoML is an automated machine learning (AutoML) framework optimized for efficient model training and hyperparameter tuning, focusing on both tabular and text data. -
12
Liger Kernel
Efficient Triton Kernels for LLM Training
Liger Kernel is a unified kernel developed by LinkedIn to streamline data science and machine learning workflows across different languages and tools. It provides a consistent interface for running code in various languages (such as Python, R, SQL) within a single Jupyter-like environment, enhancing productivity and collaboration for data scientists working in mixed-language projects. -
13
LangKit
An open-source toolkit for monitoring Language Learning Models (LLMs)
LangKit is an open-source text metrics toolkit for monitoring language models. It offers an array of methods for extracting relevant signals from the input and/or output text, which are compatible with the open-source data logging library whylogs. Productionizing language models, including LLMs, comes with a range of risks due to the infinite amount of input combinations, which can elicit an infinite amount of outputs. The unstructured nature of text poses a challenge in the ML observability... -
14
Context Engineering
A frontier, first-principles handbook
...Moving beyond traditional prompt engineering, this repository defines and explores how to craft and provide complete context payloads — not just single prompts — to large language models so they can perform tasks more reliably and intelligently. It takes inspiration from thought leaders like Andrej Karpathy and bridges theory with practical examples, offering structured guidance on context orchestration, memory, retrieval, and state control within AI workflows. With extensive materials drawn from research, surveys, and visual explanations, the project acts as both a learning resource and a reference for practitioners looking to improve model behavior by engineering richer inputs. -
15
TensorFlow Serving
Serving system for machine learning models
TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It deals with the inference aspect of machine learning, taking models after training and managing their lifetimes, providing clients with versioned access via a high-performance, reference-counted lookup table. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data. ... -
16
aie-book
Resources for AI engineers
aie-book is an open-source companion repository that supports a comprehensive guide to building real-world applications with modern AI systems, particularly those based on foundation models. It focuses on bridging the gap between theoretical machine learning knowledge and practical engineering workflows required to deploy AI systems in production. The material emphasizes system design, data pipelines, evaluation strategies, and deployment considerations rather than just model training. It explores how to work with large language models, retrieval systems, and agent-based architectures, providing a practical perspective on how AI is actually used in industry. ... -
17
LMDeploy
LMDeploy is a toolkit for compressing, deploying, and serving LLMs
LMDeploy is a toolkit designed for compressing, deploying, and serving large language models (LLMs). It offers tools and workflows to optimize LLMs for production environments, ensuring efficient performance and scalability. LMDeploy supports various model architectures and provides deployment solutions across different platforms. -
18
fugue
A unified interface for distributed computing
Fugue is a unified interface for distributed computing that lets users execute Python, Pandas, and SQL code on Spark, Dask, and Ray with minimal rewrites. -
19
Kimi k1.5
Scaling Reinforcement Learning with LLMs
Kimi-k1.5 is an advanced open-source multimodal large-language model project that explores scaling reinforcement learning with long-context chains of thought, achieving performance that rivals or surpasses state-of-the-art models on benchmarks like LiveCodeBench, AIME, and MATH-500. The project emphasizes a simplistic yet powerful framework where the context window scales up to 128k tokens, enabling reasoning that resembles planning, reflection, and correction over a much longer sequence of data than typical models. ... -
20
DB-GPT
Revolutionizing Database Interactions with Private LLM Technology
DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can be assured that there is no risk of data leakage, and your data is 100% private and secure. -
21
33 JS Concepts
33 JavaScript concepts every developer should know
33-js-concepts is a curated collection of essential JavaScript concepts that every developer should understand to strengthen their knowledge of the language. The project was originally inspired by an article by Stephen Curtis and has since grown into a community-driven learning resource. It serves as a roadmap for developers to review and master core principles such as closures, promises, prototypes, event loops, and other critical topics. While not a strict curriculum, it provides a structured path for deeper study and practical learning. ... -
22
LlamaIndex
Central interface to connect your LLM's with external data
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when... -
23
Kaggle Solutions
Collection of Kaggle Solutions and Ideas
...Because the content is organized by competition categories such as computer vision, natural language processing, tabular data, and time-series forecasting, users can explore techniques relevant to specific problem types. -
24
Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live... -
25
FastDeploy
High-performance Inference and Deployment Toolkit for LLMs and VLMs
FastDeploy is an open-source inference and deployment toolkit designed to simplify the process of running and serving deep learning models across a wide range of hardware platforms. Developed within the PaddlePaddle ecosystem, the toolkit focuses on providing high-performance deployment capabilities for modern AI models including large language models and vision-language systems. The platform enables developers to deploy trained models quickly using optimized inference pipelines that support GPUs, specialized AI accelerators, and other hardware architectures. ...
