Search Results for "self learning"
Sort By:
Showing 79 open source projects for "self learning"
View related business solutions-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
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.
-
1
NFH Self-Improvement Loop
Minimal adversarial framework for AI agent self-modification
NFH Self-Improvement Loop is a conceptual framework and implementation designed to model continuous self-improvement cycles using AI systems. It focuses on creating feedback loops where outputs are evaluated, refined, and reintroduced into the system for further improvement. The project emphasizes iterative learning, allowing systems to evolve over time through repeated evaluation and adjustment. -
2
Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. -
3
Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is... -
4
satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful... -
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.
-
5
Computer Science courses video lectures
List of Computer Science courses with video lectures
...For self-taught engineers, graduates, or career-changers, this repository acts as a roadmap: pick courses, follow them, mark completion, and build a depth of theoretical foundation. It’s especially useful when mapping out a learning path rather than relying on a single bootcamp or video series. -
6
The Data Engineering Handbook
Links to everything you'd ever want to learn about data engineering
The Data Engineering Handbook is a comprehensive, community-curated repository that aggregates essential learning resources for anyone interested in becoming a professional data engineer. Rather than being a code project itself, it’s a learning handbook that links to books, articles, tutorials, community groups, boot camps, and real-world project examples that collectively form a roadmap to mastering data engineering skills. It includes beginner and intermediate boot camps, interview guides, data cleaning and transformation resources, and curated lists of newsletters and industry communities, making it useful both for self-study and technical interview preparation. ... -
7
JavaScript Basics
Educational repository for learning JavaScript
...Separate files provide multiple-choice questions and additional coding-oriented study material. The collection is designed for self-paced learning rather than as a reusable software package. Its size and topic breadth make it suitable as a searchable reference for day-to-day JavaScript study. -
8
Python-programming-exercises
100+ Python challenging programming exercises
Python-programming-exercises is an educational repository containing more than 100 Python programming challenges. It is designed to help learners practice problem solving through short exercises rather than full applications. The exercises cover fundamentals such as strings, numbers, loops, lists, dictionaries, functions, regular expressions, file handling, classes, generators, and algorithmic thinking. Many problems include both a prompt and a suggested solution, making the repository... -
9
Saeba's Blog
Where Saeba writes his blog, he plans to write four series: JavaScript
Blog is a large Chinese-language frontend engineering knowledge base maintained by mqyqingfeng. It collects in-depth articles on JavaScript internals, practical JavaScript topics, ES6, React, and broader frontend development ideas. Rather than being a software package, it functions as a structured educational repository for developers who want to understand how JavaScript features work under the hood. Many articles focus on concepts such as execution context, scope, prototypes, closures,... -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
10
Back-End Developer Interview Questions
A list of back-end related questions
...Interviewers can select only the sections relevant to a particular role instead of following the entire list. Developers can also use the repository for self-assessment, study, and identifying gaps in their technical knowledge. -
11
EasyZSwoole
swoole, easyswoole, swoole framework
...Let developers write multi-process, step-by-step, and high-available application services with the lowest learning cost and effort. -
12
SIA
AI framework to autonomously improve the performance of any AI system
SIA is a self-improving AI framework designed to improve the performance of models or agents on benchmark tasks. It uses an iterative loop where a meta-agent creates or updates a task-specific target agent, while a feedback agent studies results and proposes improvements. The framework can refine both the harness around the task and the agent implementation itself. -
13
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... -
14
LaTeX Examples
Examples for the usage of LaTeX
...Because examples are self-contained and well-commented, users can copy, adapt, or test them without needing a large TeX toolchain. -
15
Pythonic Data Structures and Algorithms
Minimal examples of data structures and algorithms in Python
...It offers working, often well-commented code for many standard algorithmic problems — from sorting/searching to graph algorithms, dynamic programming, data structures, and more — making it a valuable resource for learning and reference. For students preparing for technical interviews, self-learners brushing up on fundamentals, or developers wanting to understand algorithm internals, this repository provides ready-to-run examples, and can serve as a sandbox to experiment, benchmark, or adapt code. Because it’s in pure Python, it’s easy to read and modify, making it accessible even to those with modest programming experience. ... -
16
WordPress Playground
Run WordPress in the browser via WebAssembly PHP
WordPress Playground is an experimental platform that allows users to run a fully functional WordPress environment entirely inside a web browser using WebAssembly, eliminating the need for a traditional PHP server or database setup. It achieves this by compiling PHP and other required components into WebAssembly, effectively creating a self-contained virtual environment that runs locally within the browser. This enables instant access to WordPress for learning, testing, and development without installation or hosting, significantly lowering the barrier to entry for new users and developers. The platform acts as a sandbox where users can experiment with themes, plugins, and configurations without affecting any live websites, making it ideal for prototyping and quality assurance workflows. ... -
17
Professional Programming
A collection of learning resources for curious software engineers
...Because it has been maintained for many years, it also acts as a kind of “canon” of articles that many engineers reference throughout their careers. The repository is especially helpful for self-taught developers or those transitioning from junior to senior roles who want a structured reading roadmap instead of random blog posts. -
18
RuVector
Self-Learning, Vector Graph Neural Network, and Database built in Rust
RuVector is part of the broader rUv ecosystem of AI engineering tools and focuses on enabling advanced vector-based processing and intelligent system development within agentic and AI-driven pipelines. The project fits into a larger vision of modular, composable AI infrastructure designed to support autonomous agents, data retrieval, and intelligent automation workflows. It emphasizes extensibility and interoperability with modern AI stacks, allowing developers to integrate vector operations... -
19
Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). ... -
20
Qwerty Learner
Words learning and English muscle memory training software
Qwerty Learner is an open-source vocabulary and typing practice application designed for people who work primarily at a keyboard. It combines word memorization with repeated English typing to strengthen accurate muscle memory. Users must retype incorrectly entered words, reducing the chance of reinforcing spelling mistakes. The application includes dictionaries for major English exams, school levels, business English, programming terms, and several language-specific APIs. Phonetic symbols... -
21
Go 101
An up-to-date (unofficial) knowledge base for Go programming
Go 101 is a series of books on Go programming. Currently, the following books are available. Go (Fundamentals) 101, which focuses on Go syntax/semantics (except custom generics related) and all kinds of runtime related things. Go Generics 101, which explains Go custom generics in detail. Go Optimizations 101, which provides some code performance optimization tricks, tips, and suggestions. Go Details & Tips 101, which collects many details and provides several tips in Go programming. These... -
22
System Design
Learn how to design systems and prepare for system design interviews
...It covers the building blocks that show up in real architectures, such as DNS, load balancing, caching, CDNs, proxies, scalability and availability tradeoffs, and storage patterns, then connects those pieces to larger system goals. The materials are geared toward clarity and repeatability, making it useful both for self-study and for quick review before interviews. It emphasizes the why behind design decisions, not just a list of components, so you can explain tradeoffs under constraints like cost, latency, reliability, and growth. Overall, it functions like a practical playbook for learning how large systems are composed, where they fail, and how to reason about improvements. -
23
Tiny CUDA Neural Networks
Lightning fast C++/CUDA neural network framework
This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning-fast "fully fused" multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers. We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared... -
24
p5.js
Client-side JS platform for artists, designers and students to express
...You can think of your whole browser page as your sketch, including HTML5 objects for text, input, video, webcam, and sound. p5.js is an interpretation of Processing for today’s web. We hold events and operate with support from the Processing Foundation. For self-learners and animators, artists, game makers, creative-technologists, curriculum planners, designers, graphic designers, graphics editors, learning experience designers, project managers, software engineer, student, teachers, university faculty members, visualization researchers, etc. -
25
Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. ...
