Best AI tools for< Learn Algorithms >

TalkToMe.AI is a comprehensive platform dedicated to artificial intelligence, offering a wide range of resources for enthusiasts and professionals alike. From interactive quizzes on various AI topics to in-depth articles on machine learning algorithms and neural networks, the website aims to educate and inspire individuals interested in the field of AI. With a focus on demystifying complex concepts and keeping users updated on the latest advancements, TalkToMe.AI serves as a trusted companion for anyone looking to explore the fascinating realm of artificial intelligence.

InstaHeadshots is an AI-powered headshot generator that creates professional-looking headshots from your selfies. It uses advanced AI algorithms to learn your facial features and generate realistic headshots that look like they were taken by a professional photographer. InstaHeadshots is trusted by the world's most innovative teams and is used by individuals and businesses alike to create high-quality headshots for a variety of purposes, including LinkedIn profiles, resumes, and social media. The process of creating a headshot with InstaHeadshots is simple and easy. Simply upload 12+ high-quality selfies, and the AI will do the rest. Within 90 minutes, you will receive amazing headshots that are indistinguishable from real photos. InstaHeadshots is a one-time payment service, with no subscription required. It is also 10x cheaper than a physical photoshoot.

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Up Learn is an AI-powered educational platform designed to help students improve their A Level grades with a guarantee of achieving A*/A results or a money-back offer. The platform offers interactive videos covering the entire curriculum, detailed progress tracking, personalized revision based on AI algorithms, exam-board specific courses, guided exam practice, and 24/7 tutor support. Up Learn aims to make learning simple and efficient, ensuring that every second of study counts towards exam success.

Zazow is a website that allows users to create algorithmic generative art. Users can select from a variety of algorithms to create unique digital artwork. The algorithms are easy to use and allow users to have total control over every aspect of the artwork. Users can save their artwork and share it with the Zazow community. They can also download their finished artwork and use it in creative ways.

Wolfram|Alpha is a computational knowledge engine that answers questions using data, algorithms, and artificial intelligence. It can perform calculations, generate graphs, and provide information on a wide range of topics, including mathematics, science, history, and culture. Wolfram|Alpha is used by students, researchers, and professionals around the world to solve problems, learn new things, and make informed decisions.

Octolens is an AI-powered social listening tool designed for B2B businesses. It leverages artificial intelligence to monitor and analyze online conversations, providing valuable insights into customer sentiment, industry trends, and competitor activities. Octolens helps businesses make data-driven decisions by tracking brand mentions, identifying key influencers, and uncovering emerging topics. With its advanced algorithms, Octolens offers a comprehensive solution for businesses looking to enhance their social media strategy and stay ahead of the competition.

Elements of AI is a free online educational platform that offers courses on artificial intelligence for non-experts. Created by MinnaLearn and the University of Helsinki, the platform aims to demystify AI by providing theory, practical exercises, and a comprehensive understanding of AI concepts. With over 1 million students from 170 countries, the courses cover topics like Introduction to AI and Building AI, encouraging a broad audience to learn about AI's impact on society and how to create AI methods.

Data Science Dojo is a globally recognized e-learning platform that offers programs in data science, data analytics, machine learning, and more. They provide comprehensive and hands-on training in various formats such as in-person, virtual instructor-led, and self-paced training. The focus is on helping students develop a think-business-first mindset to apply their data science skills effectively in real-world scenarios. With over 2500 enterprises trained, Data Science Dojo aims to make data science accessible to everyone.

Speakpal is an AI-powered language learning platform that leverages cutting-edge technology to help users improve their language skills. The platform offers interactive lessons, personalized feedback, and real-time practice sessions to enhance speaking, listening, reading, and writing abilities. With a user-friendly interface and adaptive learning algorithms, Speakpal caters to learners of all levels, from beginners to advanced speakers. Whether you're looking to learn a new language for travel, work, or personal enrichment, Speakpal provides a comprehensive and engaging learning experience.

Nativish is an AI-powered writing tool that enables users to produce content in any language at a native-level proficiency. The application leverages advanced natural language processing algorithms to assist users in crafting high-quality written material effortlessly. With Nativish, language barriers are overcome, and users can create compelling and authentic content in multiple languages with ease.

Proscia is a leading provider of digital pathology solutions for the modern laboratory. Its flagship product, Concentriq, is an enterprise pathology platform that enables anatomic pathology laboratories to achieve 100% digitization and deliver faster, more precise results. Proscia also offers a range of AI applications that can be used to automate tasks, improve diagnostic accuracy, and accelerate research. The company's mission is to perfect cancer diagnosis with intelligent software that changes the way the world practices pathology.

The website offers a personalized GPT service that simplifies AI-powered Twitter conversations. Users can easily engage in AI-driven interactions on Twitter through this platform. The service is designed to enhance user experience and streamline communication on social media. With a focus on providing tailored solutions for Twitter users, the website aims to revolutionize how individuals engage with AI technology in their daily interactions. Powered by advanced AI algorithms, the service ensures efficient and effective communication in the digital realm.

Co-Op is a self-therapy application that leverages AI technology to assist users in their mental health journey. The app provides a platform for users to learn, reflect, and grow with the help of AI algorithms. It aims to support individuals in improving their well-being and emotional health through personalized guidance and insights.

Amazon Science is a research and development organization within Amazon that focuses on developing new technologies and products in the fields of artificial intelligence, machine learning, and computer science. The organization is home to a team of world-renowned scientists and engineers who are working on a wide range of projects, including developing new algorithms for machine learning, building new computer vision systems, and creating new natural language processing tools. Amazon Science is also responsible for developing new products and services that use these technologies, such as the Amazon Echo and the Amazon Fire TV.

Wriiter is an AI-powered writing tool that helps users create high-quality content quickly and effortlessly. It uses advanced natural language processing algorithms to generate text that is coherent, engaging, and error-free. With Wriiter, users can save time and effort in writing blog posts, articles, essays, and more. The tool provides suggestions for improving the clarity and readability of the content, making it suitable for writers of all levels. Wriiter is designed to be user-friendly and intuitive, allowing users to focus on their ideas and creativity without worrying about grammar or structure.

BabblerAI is an advanced artificial intelligence tool designed to assist businesses in analyzing and extracting valuable insights from large volumes of text data. The application utilizes natural language processing and machine learning algorithms to provide users with actionable intelligence and automate the process of information extraction. With BabblerAI, users can streamline their data analysis workflows, uncover trends and patterns, and make data-driven decisions with confidence. The tool is user-friendly and offers a range of features to enhance productivity and efficiency in data analysis tasks.

Prayer AI is a platform that utilizes advanced AI algorithms to assist users in creating personalized prayers tailored to their specific needs. The website offers a user-friendly interface for generating heartfelt and sincere prayers for healing, peace, and strength. Users can access a vast library of prayer resources, writing guides, and inspirational articles to enhance their spiritual journey. Prayer AI aims to address common challenges in prayer crafting, such as finding the right words, needing guidance and comfort, limited time and focus, inconsistent prayer styles, and the lack of a personal touch in generic prayers.

Million Dollar Edit 2.0 is an AI-powered editing tool that helps you write and edit faster and more efficiently. With its advanced AI algorithms, Million Dollar Edit 2.0 can help you identify and correct errors, improve your writing style, and even generate new content. Whether you're a student, a professional writer, or anyone who wants to improve their writing skills, Million Dollar Edit 2.0 can help you take your writing to the next level.

AI Lawyer Lab is a platform that enables legal professionals to leverage artificial intelligence technology to enhance their legal services. By utilizing AI algorithms, users can streamline legal processes, analyze vast amounts of legal data efficiently, and generate insights to support decision-making. The platform empowers lawyers to transform their legal expertise into innovative AI solutions, ultimately improving the quality and efficiency of legal services.

This repository is a collection of free resources and a roadmap designed to help individuals learn Machine Learning and Artificial Intelligence concepts by providing key concepts, building blocks, roles, a learning roadmap, courses, certifications, books, tools & frameworks, research blogs, applied blogs, practice problems, communities, YouTube channels, newsletters, and must-read papers. It covers a wide range of topics from supervised learning to MLOps, offering guidance on learning paths, practical experience, and job interview preparation.

This repository provides code samples in Python, C#, REST, and JavaScript for vector support in Azure AI Search. It includes demos for various languages showcasing vectorization of data, creating indexes, and querying vector data. Additionally, it offers tools like Azure AI Search Lab for experimenting with AI-enabled search scenarios in Azure and templates for deploying custom chat-with-your-data solutions. The repository also features documentation on vector search, hybrid search, creating and querying vector indexes, and REST API references for Azure AI Search and Azure OpenAI Service.

The ai-workshop-code repository contains code examples and tutorials for various artificial intelligence concepts and algorithms. It serves as a practical resource for individuals looking to learn and implement AI techniques in their projects. The repository covers a wide range of topics, including machine learning, deep learning, natural language processing, computer vision, and reinforcement learning. By exploring the code and following the tutorials, users can gain hands-on experience with AI technologies and enhance their understanding of how these algorithms work in practice.

Mastering GitHub Copilot for AI Paired Programming is a comprehensive course designed to equip you with the skills and knowledge necessary to harness the power of GitHub Copilot, an AI-driven coding assistant. Through a series of engaging lessons, you will learn how to seamlessly integrate GitHub Copilot into your workflow, leveraging its autocompletion, customizable features, and advanced programming techniques. This course is tailored to provide you with a deep understanding of AI-driven algorithms and best practices, enabling you to enhance code quality and accelerate your coding skills. By embracing the transformative power of AI paired programming, you will gain the tools and confidence needed to succeed in today's dynamic software development landscape.

sktime is a Python library for time series analysis that provides a unified interface for various time series learning tasks such as classification, regression, clustering, annotation, and forecasting. It offers time series algorithms and tools compatible with scikit-learn for building, tuning, and validating time series models. sktime aims to enhance the interoperability and usability of the time series analysis ecosystem by empowering users to apply algorithms across different tasks and providing interfaces to related libraries like scikit-learn, statsmodels, tsfresh, PyOD, and fbprophet.

Fueling-Ambitions-Via-Book-Discoveries is an Advanced Machine Learning & AI Course designed for students, professionals, and AI researchers. The course integrates rigorous theoretical foundations with practical coding exercises, ensuring learners develop a deep understanding of AI algorithms and their applications in finance, healthcare, robotics, NLP, cybersecurity, and more. Inspired by MIT, Stanford, and Harvard’s AI programs, it combines academic research rigor with industry-standard practices used by AI engineers at companies like Google, OpenAI, Facebook AI, DeepMind, and Tesla. Learners can learn 50+ AI techniques from top Machine Learning & Deep Learning books, code from scratch with real-world datasets, projects, and case studies, and focus on ML Engineering & AI Deployment using Django & Streamlit. The course also offers industry-relevant projects to build a strong AI portfolio.

Amazon SageMaker Course is a comprehensive guide for AWS Certified Machine Learning Specialty (MLS-C01) that covers training, optimizing, deploying, and integrating machine learning models in the AWS cloud. The course includes hands-on experience with AWS built-in algorithms, Bring Your Own models, and ready-to-use AI capabilities. It also provides a complete guide to AWS Certified Machine Learning – Specialty certification, along with a high-quality timed practice test. Participants will learn how to integrate trained models into their applications and receive prompt support through the course Q&A forum and private messaging.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

LLM-as-HH is a codebase that accompanies the paper ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution. It introduces Language Hyper-Heuristics (LHHs) that leverage LLMs for heuristic generation with minimal manual intervention and open-ended heuristic spaces. Reflective Evolution (ReEvo) is presented as a searching framework that emulates the reflective design approach of human experts while surpassing human capabilities with scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. The tool can improve various algorithms on problems like Traveling Salesman Problem, Capacitated Vehicle Routing Problem, Orienteering Problem, Multiple Knapsack Problems, Bin Packing Problem, and Decap Placement Problem in both black-box and white-box settings.

This repository contains a collection of sample applications and Jupyter Notebooks for hands-on experience with Pinecone vector databases and common AI patterns, tools, and algorithms. It includes production-ready examples for review and support, as well as learning-optimized examples for exploring AI techniques and building applications. Users can contribute, provide feedback, and collaborate to improve the resource.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

This repository is the workshop repository for the Causal Modeling in Machine Learning Workshop on Altdeep.ai. The material is open source and free. The course covers causality in model-based machine learning, Bayesian modeling, interventions, counterfactual reasoning, and deep causal latent variable models. It aims to equip learners with the ability to build causal reasoning algorithms into decision-making systems in data science and machine learning teams within top-tier technology organizations.

Godot RL Agents is an open-source package that facilitates the integration of Machine Learning algorithms with games created in the Godot Engine. It provides interfaces for popular RL frameworks, support for memory-based agents, 2D and 3D games, AI sensors, and is licensed under MIT. Users can train agents in the Godot editor, create custom environments, export trained agents in ONNX format, and utilize advanced features like different RL training frameworks.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

llm-compressor is an easy-to-use library for optimizing models for deployment with vllm. It provides a comprehensive set of quantization algorithms, seamless integration with Hugging Face models and repositories, and supports mixed precision, activation quantization, and sparsity. Supported algorithms include PTQ, GPTQ, SmoothQuant, and SparseGPT. Installation can be done via git clone and local pip install. Compression can be easily applied by selecting an algorithm and calling the oneshot API. The library also offers end-to-end examples for model compression. Contributions to the code, examples, integrations, and documentation are appreciated.

FLUTE (Flexible Lookup Table Engine for LUT-quantized LLMs) is a tool designed for uniform quantization and lookup table quantization of weights in lower-precision intervals. It offers flexibility in mapping intervals to arbitrary values through a lookup table. FLUTE supports various quantization formats such as int4, int3, int2, fp4, fp3, fp2, nf4, nf3, nf2, and even custom tables. The tool also introduces new quantization algorithms like Learned Normal Float (NFL) for improved performance and calibration data learning. FLUTE provides benchmarks, model zoo, and integration with frameworks like vLLM and HuggingFace for easy deployment and usage.

A curated list of open-ended learning AI resources focusing on algorithms that invent new and complex tasks endlessly, inspired by human advancements. The repository includes papers, safety considerations, surveys, perspectives, and blog posts related to open-ended AI research.

Your algorithm study pal!

Sharpen your Roux solving skills with RouxGPT—your go-to for swift CMLL algorithms, effective training, and expert troubleshooting.

Effective DS & Algorithms coach (type "help" to start). "May the Forge be with you! 🚀"

Tutor for data structures, algorithms, LeetCode, and SQL.

Golang, algorithms, data structures & HTMX expert

Rubik's Cube Teacher, focused on personalized learning.

Guiding you from beginner to expert in Competitive Programming, or simply explain solutions to your problems.

競技プログラミングコーチ。

Your gateway to mastering Quantum Algorithms with expert insights, practical applications, and engaging learning.

Teaches quantum algorithms, communication, and circuit models to students.

Get into FAANG. Practice with an AI expert in algorithms, data structures, and system design. Do a mock interview and improve.

A pro level cuber who guides you end to end on how to solve a Rubik’s cube. Learn different methods as you level up. Just follow the algorithms!. Upload 6 faces of the cube onto SolveRubiks.

I assist in studying for an algorithms exam, guiding through concepts and problems.

I write Qiskit code for quantum algorithms with annotations.

Assistant for all data structures and algorithms related questions

帮助用户学习和掌握编程知识

A Quantum Circuit Lecturer and Generator

Quantum physics and computing explainer and developer

Your quantum assistant, learning quantum anytime, anywhere

Tutor on purely functional data structures and functional programming