Best AI tools for< Learn Wing Chun >

Ragie is a fully managed RAG-as-a-Service platform designed for developers. It offers easy-to-use APIs and SDKs to help developers get started quickly, with advanced features like LLM re-ranking, summary index, entity extraction, flexible filtering, and hybrid semantic and keyword search. Ragie allows users to connect directly to popular data sources like Google Drive, Notion, Confluence, and more, ensuring accurate and reliable information delivery. The platform is led by Craft Ventures and offers seamless data connectivity through connectors. Ragie simplifies the process of data ingestion, chunking, indexing, and retrieval, making it a valuable tool for AI applications.

pdf → gpt is a tool that allows users to summarize large PDFs using GPT. It is a web-based application that is easy to use and can be accessed from any device with an internet connection. Users simply need to upload a PDF file to the application and then select the desired summary length. The application will then generate a summary of the PDF file that is tailored to the user's needs.

Scrivvy is a web-based tool that helps users summarize YouTube videos. It uses artificial intelligence to generate concise summaries of videos of any length. Users can search their video history to quickly find the summaries they need. Scrivvy also breaks down long videos into short chunks, making it easier to understand the content. Users can try the service risk-free with free credits when they sign up.

Shield AI is a defense technology company building the world's best AI pilot, Hivemind, to enable swarms of drones and aircraft to operate autonomously without GPS, communications, or a pilot. Their mission is to protect service members and civilians with intelligent systems. Hivemind is a top gun for every aircraft, more than just preset behaviors and waypoints. Like a human pilot, Hivemind reads and reacts to the battlefield and does not require GPS, waypoints, or prior knowledge to make decisions. It is the first and only fully autonomous AI pilot deployed in combat since 2018. From indoor building clearance with quadcopters to integrated air defense breach with fixed-wing drones and F-16 dogfights, Hivemind learns and autonomously executes missions. Shield AI also offers V-BAT teams, which enable multiple V-BATs to autonomously execute missions in electronically contested environments while reading and reacting to adversaries, the environment, and the other V-BATs executing the mission. V-BAT is combat-tested and deployed since 2018, and it flies in a class of its own. It's the most tactical, most logistically simple VTOL aircraft in the world, capable of executing group 2 to group 5 mission sets. It is the UAS of choice for US and allied forces. Nova 2 is built for the future fight and has proven its value in close-quarters combat with the most demanding customers in the world – on the most high-profile missions. Hivemind gives Nova 2 full autonomy - no GPS, no comms, no pilot needed.

Learning Robots is an educational platform that aims to make artificial intelligence accessible to everyone, from students to professionals. The platform offers a range of educational solutions, including the AlphAI software and educational robots, to help users understand and engage with AI concepts in a fun and interactive way. Learning Robots provides resources for learning AI at different levels, whether in classrooms or corporate settings, to facilitate the learning process and demystify AI technology.

Learning Copilot is an AI-powered platform designed to assist users in enhancing their learning experience. It leverages artificial intelligence to provide personalized recommendations, interactive study materials, and real-time feedback to help users improve their knowledge retention and academic performance. With a user-friendly interface and advanced algorithms, Learning Copilot aims to revolutionize the way people learn by making education more engaging, efficient, and effective.

The website offers a brand new course titled 'Prompt Engineering for Everyone' to help users master the language of AI. With over 100 courses and 20+ learning paths, users can learn AI, Data Science, and other emerging technologies. The platform provides hands-on content designed by expert instructors, allowing users to gain practical, industry-relevant knowledge and skills. Users can earn certificates to showcase their expertise and build projects to demonstrate their skills. Trusted by 3 million learners globally, the platform offers a community of learners with a proven track record of success.

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. The deep learning textbook can now be ordered on Amazon. For up to date announcements, join our mailing list.

Practical Deep Learning for Coders is a free course designed for individuals with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. The course covers topics such as building and training deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems. It is based on a 5-star rated book and does not require any special hardware or software. The course is led by Jeremy Howard, a renowned expert in machine learning and the President and Chief Scientist of Kaggle.

Tech & Learning is an AI tool designed to provide ideas, tools, and resources for educators to transform education. It offers a wide range of content, including news, classroom tools, higher education events, professional development resources, and contests. The platform covers topics such as AI, cybersecurity, gamification, and innovative teaching methods, aiming to support effective teaching and learning practices.

CYPHER Learning is a leading AI-powered learning platform offering solutions for academia, business, and entrepreneurs. The platform provides features such as course development, AI media options, personalized skills development, gamification, automation, integrations, reporting & analytics, and more. CYPHER Learning focuses on human-centric learning, offers enterprise-class connections, supports over 50 languages, and provides customizable and pre-built courses. The platform aims to enhance learning experiences through AI innovation and automation.

Great Learning is an online platform offering a wide range of courses, PG certificates, and degree programs in various domains such as AI & Machine Learning, Data Science, Business Analytics, Cloud Computing, Cyber Security, Software Development, Digital Marketing, Design, MBA, and Masters. The platform provides opportunities to learn from top universities, offers career support, success stories, and enterprise solutions. With a focus on AI and Machine Learning, Great Learning aims to elevate expertise and provide transformative programs to help individuals enhance their skills and advance their careers.

Techstrong Learning is an AI-powered platform that hosts various virtual and in-person events focusing on technology, innovation, and enterprise development. The platform aims to bring together professionals from diverse backgrounds to explore the transformative impact of AI across different business landscapes. With a special focus on enterprise, DevOps, and AI development, Techstrong Learning offers a holistic learning experience through conferences, summits, and virtual events. Participants gain valuable insights into leveraging AI for decision-making, cost efficiency, and customer engagement, equipping them with strategic knowledge to navigate the future of AI-driven business dynamics.

The Top 100 Tools for Learning 2024 website provides a comprehensive list of the most popular tools for learning based on the 18th annual survey results. The list includes a variety of tools ranging from video hosting platforms to AI chatbots and educational resources. Users can explore and discover new tools to enhance their learning experience in different areas.

The website is a comprehensive resource hub for learning and mastering end-to-end testing using the Playwright automation framework. It offers a variety of content such as blog posts, tutorials, videos, and a QA Wiki with definitions of common testing terms. Users can also ask questions related to Playwright and access a Discord forum for discussions. Additionally, there is a browser extension available for generating Playwright locators, and a section dedicated to QA jobs and automation opportunities.

Synaptiq Learning is an AI-powered learning platform designed for studying medicine. It utilizes cutting-edge algorithms to personalize review schedules through evidence-backed spaced repetition techniques. The platform offers features such as personalized review schedules, personal stats tracking, dark mode for night learning, deck sharing for collaboration, algorithm tuning for customization, and controller support for an enhanced study experience.

Breakout Learning is an AI-powered educational platform that transforms traditional case studies into engaging, multifaceted experiences. It empowers professors with AI insights into small-group discussions, enabling them to customize lectures and foster deeper student comprehension. Students benefit from rich content, peer-led discussions, and AI assessment that provides personalized feedback and tracks their progress.

My Learning Space AI is an all-in-one immersive learning platform that offers comprehensive application guides, interview tips, tutoring services, and expert coaching to help students boost their chances of admission to various graduate programs. The platform provides AI-driven feedback on mock video interviews, personalized coaching, and assistance in aligning responses with program expectations to enhance articulation, confidence, and strategic alignment.

FavTutor AI Learning is an AI-powered tool designed to help users master programming skills through personalized learning experiences. The tool utilizes artificial intelligence algorithms to provide tailored lessons, practice exercises, and feedback to enhance the user's programming proficiency. With FavTutor AI Learning, users can improve their coding abilities at their own pace and convenience, making it an ideal platform for both beginners and experienced programmers seeking to enhance their skills.

Zora is an adaptive storytelling platform for learning that offers personalized and engaging reading experiences for users of all ages. Named after Zora Neale Hurston, the platform allows users to create their own characters, explore genres matching their interests, and deepen reading comprehension skills and vocabulary through adaptive storytelling. Zora aims to make learning fun and effective by providing a gamified approach to skill mastery and comprehension. The platform offers age-appropriate and safe content, with a focus on continuous development of interactive features to enhance the reading experience.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

LLMInterviewQuestions is a repository containing over 100+ interview questions for Large Language Models (LLM) used by top companies like Google, NVIDIA, Meta, Microsoft, and Fortune 500 companies. The questions cover various topics related to LLMs, including prompt engineering, retrieval augmented generation, chunking, embedding models, internal working of vector databases, advanced search algorithms, language models internal working, supervised fine-tuning of LLM, preference alignment, evaluation of LLM system, hallucination control techniques, deployment of LLM, agent-based system, prompt hacking, and miscellaneous topics. The questions are organized into 15 categories to facilitate learning and preparation.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

The W3C Cognitive AI Community Group focuses on advancing Cognitive AI through collaboration on defining use cases, open source implementations, and application areas. The group aims to demonstrate the potential of Cognitive AI in various domains such as customer services, healthcare, cybersecurity, online learning, autonomous vehicles, manufacturing, and web search. They work on formal specifications for chunk data and rules, plausible knowledge notation, and neural networks for human-like AI. The group positions Cognitive AI as a combination of symbolic and statistical approaches inspired by human thought processes. They address research challenges including mimicry, emotional intelligence, natural language processing, and common sense reasoning. The long-term goal is to develop cognitive agents that are knowledgeable, creative, collaborative, empathic, and multilingual, capable of continual learning and self-awareness.

PostgresML is a powerful Postgres extension that seamlessly combines data storage and machine learning inference within your database. It enables running machine learning and AI operations directly within PostgreSQL, leveraging GPU acceleration for faster computations, integrating state-of-the-art large language models, providing built-in functions for text processing, enabling efficient similarity search, offering diverse ML algorithms, ensuring high performance, scalability, and security, supporting a wide range of NLP tasks, and seamlessly integrating with existing PostgreSQL tools and client libraries.

EvalAI is an open-source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale. It provides a central leaderboard and submission interface, making it easier for researchers to reproduce results mentioned in papers and perform reliable & accurate quantitative analysis. EvalAI also offers features such as custom evaluation protocols and phases, remote evaluation, evaluation inside environments, CLI support, portability, and faster evaluation.

The 'awesome-ai-llm4education' repository is a curated list of papers related to artificial intelligence (AI) and large language models (LLM) for education. It collects papers from top conferences, journals, and specialized domain-specific conferences, categorizing them based on specific tasks for better organization. The repository covers a wide range of topics including tutoring, personalized learning, assessment, material preparation, specific scenarios like computer science, language, math, and medicine, aided teaching, as well as datasets and benchmarks for educational research.

Deep Lake is a Database for AI powered by a storage format optimized for deep-learning applications. Deep Lake can be used for: 1. Storing data and vectors while building LLM applications 2. Managing datasets while training deep learning models Deep Lake simplifies the deployment of enterprise-grade LLM-based products by offering storage for all data types (embeddings, audio, text, videos, images, pdfs, annotations, etc.), querying and vector search, data streaming while training models at scale, data versioning and lineage, and integrations with popular tools such as LangChain, LlamaIndex, Weights & Biases, and many more. Deep Lake works with data of any size, it is serverless, and it enables you to store all of your data in your own cloud and in one place. Deep Lake is used by Intel, Bayer Radiology, Matterport, ZERO Systems, Red Cross, Yale, & Oxford.

DeepLearning-Interview-Awesome-2024 is a repository that covers various topics related to deep learning, computer vision, big models (LLMs), autonomous driving, smart healthcare, and more. It provides a collection of interview questions with detailed explanations sourced from recent academic papers and industry developments. The repository is aimed at assisting individuals in academic research, work innovation, and job interviews. It includes six major modules covering topics such as large language models (LLMs), computer vision models, common problems in computer vision and perception algorithms, deep learning basics and frameworks, as well as specific tasks like 3D object detection, medical image segmentation, and more.

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.

Magma is a powerful and flexible framework for building scalable and efficient machine learning pipelines. It provides a simple interface for creating complex workflows, enabling users to easily experiment with different models and data processing techniques. With Magma, users can streamline the development and deployment of machine learning projects, saving time and resources.

ShapeLLM is the first 3D Multimodal Large Language Model designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. It supports single-view colored point cloud input and introduces a robust 3D QA benchmark, 3D MM-Vet, encompassing various variants. The model extends the powerful point encoder architecture, ReCon++, achieving state-of-the-art performance across a range of representation learning tasks. ShapeLLM can be used for tasks such as training, zero-shot understanding, visual grounding, few-shot learning, and zero-shot learning on 3D MM-Vet.

PythonDataScienceFullThrottle is a comprehensive repository containing various Python scripts, libraries, and tools for data science enthusiasts. It includes a wide range of functionalities such as data preprocessing, visualization, machine learning algorithms, and statistical analysis. The repository aims to provide a one-stop solution for individuals looking to dive deep into the world of data science using Python.

This repo contains official implementations of **Coach AI Badminton Project** from Advanced Database System Laboratory, National Yang Ming Chiao Tung University supervised by Prof. Wen-Chih Peng. The high-level concepts of each project are as follows: 1. Visualization Platform published at _Physical Education Journal 2020_ aims to construct a platform that can be used to illustrate the data from matches. 2. Shot Influence and Extension Work published at _ICDM-21_ and _ACM TIST 2022_ , respectively introduce a framework with a shot encoder, a pattern extractor, and a rally encoder to capture long short-term dependencies for evaluating players' performance of each shot. 3. Stroke Forecasting published at _AAAI-22_ proposes the first stroke forecasting task to predict the future strokes of both players based on the given strokes by ShuttleNet, a position-aware fusion of rally progress and player styles framework. 4. Strategic Environment published at _AAAI-23 Student Abstract_ designs a safe and reproducible badminton environment for turn-based sports, which simulates rallies with different angles of view and designs the states, actions, and training procedures. 5. Movement Forecasting published at _AAAI-23_ proposes the first movement forecasting task, which contains not only the goal of stroke forecasting but also the movement of players, by DyMF, a novel dynamic graphs and hierarchical fusion model based on the proposed player movements (PM) graphs. 6. CoachAI-Challenge-IJCAI2023 is a badminton challenge (CC4) hosted at _IJCAI-23_. Please find the website for more details. 7. ShuttleSet published at _KDD-23_ is the largest badminton singles dataset with stroke-level records. - An extension dataset ShuttleSet22 published at _IJCAI-24 Demo & IJCAI-23 IT4PSS Workshop_ is also released. 8. CoachAI Badminton Environment published at _AAAI-24 Student Abstract and Demo, DSAI4Sports @ KDD 2023_ is a reinforcement learning (RL) environment tailored for AI-driven sports analytics, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities.

ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.

Embedchain is an Open Source Framework for personalizing LLM responses. It simplifies the creation and deployment of personalized AI applications by efficiently managing unstructured data, generating relevant embeddings, and storing them in a vector database. With diverse APIs, users can extract contextual information, find precise answers, and engage in interactive chat conversations tailored to their data. The framework follows the design principle of being 'Conventional but Configurable' to cater to both software engineers and machine learning engineers.

RAG Web UI is an intelligent dialogue system based on RAG (Retrieval-Augmented Generation) technology. It helps enterprises and individuals build intelligent Q&A systems based on their own knowledge bases. By combining document retrieval and large language models, it delivers accurate and reliable knowledge-based question-answering services. The system is designed with features like intelligent document management, advanced dialogue engine, and a robust architecture. It supports multiple document formats, async document processing, multi-turn contextual dialogue, and reference citations in conversations. The architecture includes a backend stack with Python FastAPI, MySQL + ChromaDB, MinIO, Langchain, JWT + OAuth2 for authentication, and a frontend stack with Next.js, TypeScript, Tailwind CSS, Shadcn/UI, and Vercel AI SDK for AI integration. Performance optimization includes incremental document processing, streaming responses, vector database performance tuning, and distributed task processing. The project is licensed under the Apache-2.0 License and is intended for learning and sharing RAG knowledge only, not for commercial purposes.

Scholarly techniques, training, and philosophy.

Elearning creator, instructor, script & storyboard guide

Expert learning guide with captivating engagement skills.

An all-in-one, consultative L&D expert AI helping you build impactful, customized learning solutions for your organization.

A Learning Experience Designer (LXD) - in support of LXDs and those who work with them.

Your personal A.I. learning hero when creating interactive e-learning content

A guru teaching happiness through tales.

Enhances organizational performance through employee learning and development initiatives.

Discover how to make learning easy and fun! 📚 You can ask the question in any language 😁

I help break down learning goals into manageable steps.

upload Your UI and translate to PRD and user stories for your work

Creates measurable objectives from educational documents and suggests assessments based on those LO's. PDF's work best.

Interactive ML/AI tutor providing structured daily lessons.

Guiding you through the depths of deep learning with accuracy and respect.

I'm a ML engineer who formats code.

I make fun, engaging learning material for any language, topic and level!

A Data Science interviewer

The most comprehensive guide to ML theory and applications.

Your tailored guide to special needs education, growth and development.