Best AI tools for< Understand Techniques >

AlphaCode is an AI-powered programming assistant that can help you write code faster and more efficiently. It uses advanced machine learning techniques to understand your code and generate suggestions that can help you improve your code quality and performance.

ChatPDF is an AI-powered tool that allows users to interact with PDFs in a conversational manner. It uses advanced natural language processing and machine learning techniques to understand user queries and provide relevant information from the PDF document. ChatPDF is designed to make it easier and faster for users to access and understand information from PDFs, particularly in the context of research, education, and professional settings.

Owlift is a website that uses AI to simplify complex topics and make them more accessible to everyone. It offers a variety of tools for teachers and students, including lesson planners, mind maps, discussion question generators, and more. Owlift is designed to be easy to use and understand, and it can be used by people of all ages and backgrounds.

ELI5 is an AI-powered website that simplifies complex topics into easy-to-understand explanations. It uses natural language processing to break down concepts into clear and concise language, making them accessible to people of all ages and backgrounds. ELI5 covers a wide range of subjects, from science and technology to history and culture. It also offers a variety of tools for educators, including lesson plans, discussion questions, and quizzes.

CodeMate is an AI pair programmer tool designed to help developers write error-free code faster and more efficiently. It offers features such as code analysis, debugging assistance, code refactoring, and code review using advanced AI algorithms and machine learning techniques. CodeMate supports various programming languages and provides a secure environment for developers to work on their projects. With a user-friendly interface and collaborative features, CodeMate aims to streamline the coding process and enhance productivity for individual developers, teams, and enterprises.

Character.ai is an AI tool that offers personalized AI solutions for various aspects of your daily life. It leverages artificial intelligence to provide tailored recommendations and assistance to enhance your productivity and efficiency. Whether you need help with time management, decision-making, or creative tasks, Character.ai is designed to adapt to your needs and preferences. By utilizing advanced algorithms and machine learning techniques, this AI tool aims to simplify complex processes and streamline your daily routines.

Objective is an AI-native search platform designed for developers to build modern search experiences for web and mobile applications. It offers a multimodal search API that understands human language, images, and text relationships. The platform integrates various search techniques to provide natural and relevant search results, even with inconsistent data. Objective is trusted by great companies and accelerates data science roadmaps through its efficient search capabilities.

Dreamora is an AI-powered dream interpretation application that provides accurate and comprehensive interpretations of dreams. It utilizes advanced artificial intelligence techniques and draws upon the knowledge of renowned dream interpreters like Ibn Sirin and Al-Nabulsi. By simply entering your dream into the application, you can receive a free and instant interpretation within seconds. Dreamora's interpretations consider all aspects of your dream, including the location, characters, and emotions, to offer the most precise results possible.

Prompt Engineering is a discipline focused on developing and optimizing prompts to efficiently utilize language models (LMs) for various applications and research topics. It involves skills to understand the capabilities and limitations of large language models, improving their performance on tasks like question answering and arithmetic reasoning. Prompt engineering is essential for designing robust prompting techniques that interact with LLMs and other tools, enhancing safety and building new capabilities by augmenting LLMs with domain knowledge and external tools.

DeepSeek R1 is a revolutionary open-source AI model for advanced reasoning that outperforms leading AI models in mathematics, coding, and general reasoning tasks. It utilizes a sophisticated MoE architecture with 37B active/671B total parameters and 128K context length, incorporating advanced reinforcement learning techniques. DeepSeek R1 offers multiple variants and distilled models optimized for complex problem-solving, multilingual understanding, and production-grade code generation. It provides cost-effective pricing compared to competitors like OpenAI o1, making it an attractive choice for developers and enterprises.

The Visual Computing & Artificial Intelligence Lab at TUM is a group of research enthusiasts advancing cutting-edge research at the intersection of computer vision, computer graphics, and artificial intelligence. Our research mission is to obtain highly-realistic digital replica of the real world, which include representations of detailed 3D geometries, surface textures, and material definitions of both static and dynamic scene environments. In our research, we heavily build on advances in modern machine learning, and develop novel methods that enable us to learn strong priors to fuel 3D reconstruction techniques. Ultimately, we aim to obtain holographic representations that are visually indistinguishable from the real world, ideally captured from a simple webcam or mobile phone. We believe this is a critical component in facilitating immersive augmented and virtual reality applications, and will have a substantial positive impact in modern digital societies.

AI Elon is an AI-powered chatbot that provides users with information and advice on a wide range of topics. The chatbot is powered by advanced machine learning algorithms and natural language processing techniques, which allow it to understand and generate human-like text. AI Elon is also capable of continual learning, which means that it can evolve and adapt over time, staying updated with the latest news, videos, articles, and datasets.

Synthesis is a web-based application that allows users to create realistic-sounding synthetic speech from text. The application uses a variety of AI techniques, including natural language processing and machine learning, to generate speech that is both natural-sounding and easy to understand. Synthesis can be used for a variety of purposes, including creating voiceovers for videos, podcasts, and presentations.

Design Sparks is an AI-powered creativity tool that helps users generate new ideas and solve design problems. The tool uses a variety of AI techniques, including machine learning and natural language processing, to understand user input and generate relevant ideas. Design Sparks is designed to be used by a wide range of users, from designers and engineers to marketers and business professionals. The tool is easy to use and can be accessed through a web-based interface.

Durable is a custom software development platform powered by generative AI. It enables users to create tailored software solutions without writing any code. The platform is designed to be accessible to users of all technical abilities and leverages advanced AI techniques to generate deploy-ready software that meets specific user needs and earns their trust. The team behind Durable comprises experienced founders, venture capital investors, and AI research leaders. Their AI technology combines deep learning and symbolic AI to understand user intent, validate assumptions, and continuously learn and reason. Durable is committed to developing the next chapter of AI and welcomes inquiries from driven and enthusiastic individuals interested in shaping the future of software development.

Kumo is an AI-powered platform that helps businesses personalize customer experiences, acquire new customers, understand customer behavior, improve planning and monitoring, resolve data inconsistencies, fight fraud and abuse, detect money laundering, and empower data scientists with advanced techniques. It offers cutting-edge solutions for various AI and machine learning tasks, such as predictive modeling, anomaly detection, entity resolution, and graph embeddings. Kumo's capabilities are designed to enhance customer interactions, optimize marketing campaigns, and provide valuable insights for businesses across different industries.

Free AI Therapist is a free online therapy service that uses artificial intelligence to provide support and guidance to users. The service is not intended to replace professional therapy, but rather to provide a convenient and accessible way for users to get help with mental health issues. Free AI Therapist uses a variety of AI techniques, including natural language processing and machine learning, to understand user input and provide tailored responses. The service is designed to be empathetic and supportive, and it can help users with a variety of mental health issues, including anxiety, depression, and stress.

AlphaResearch is an AI-powered search engine and research platform for investors. It provides access to millions of global filings, transcripts, press releases, and reports, and uses machine learning and NLP techniques to extract insights from text data. AlphaResearch helps investors save time on research, understand market sentiment, and make better investment decisions.

AskCodi is an AI coding assistant that helps developers write code more efficiently. It provides real-time suggestions, code completion, and error detection to streamline the coding process. With its advanced algorithms, AskCodi can understand the context of the code and offer relevant recommendations. By leveraging machine learning techniques, AskCodi continuously learns and improves its suggestions to better assist developers in their coding tasks.

Refact.ai is an open-source AI coding assistant that offers a range of features including code completion, refactoring, and chat. It supports various LLMs such as GPT-4 and Code LLama, allowing users to choose the model that best suits their needs. Refact understands the context of the codebase using a fill-in-the-middle technique, providing relevant suggestions. Users can opt for a self-hosted version or adjust privacy settings for the plugin.

A curated collection of papers and resources on Mixture of Experts in Large Language Models. The repository provides a chronological overview of several representative Mixture-of-Experts (MoE) models in recent years, structured according to release dates. It covers MoE models from various domains like Natural Language Processing (NLP), Computer Vision, Multimodal, and Recommender Systems. The repository aims to offer insights into Inference Optimization Techniques, Sparsity exploration, Attention mechanisms, and safety enhancements in MoE models.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

This repository is part of the Certified Agentic & Robotic AI Engineer program, covering the first quarter of the course work. It focuses on Modern AI Python Programming, emphasizing static typing for robust and scalable AI development. The course includes modules on Python fundamentals, object-oriented programming, advanced Python concepts, AI-assisted Python programming, web application basics with Python, and the future of Python in AI. Upon completion, students will be able to write proficient Modern Python code, apply OOP principles, implement asynchronous programming, utilize AI-powered tools, develop basic web applications, and understand the future directions of Python in AI.

Hands-On Large Language Models is a repository containing code examples from the book 'The Illustrated LLM Book' by Jay Alammar and Maarten Grootendorst. The repository provides practical tools and concepts for using Large Language Models with over 250 custom-made figures. It covers topics such as language model introduction, tokens and embeddings, transformer LLMs, text classification, text clustering, prompt engineering, text generation techniques, semantic search, multimodal LLMs, text embedding models, fine-tuning representation models, and fine-tuning generation models. The examples are designed to be run on Google Colab with T4 GPU support, but can be adapted to other cloud platforms as well.

The 'understand-r1-zero' repository focuses on understanding R1-Zero-like training from a critical perspective. It provides insights into base models and reinforcement learning components, highlighting findings and proposing solutions for biased optimization. The repository offers a minimalist recipe for R1-Zero training, detailing the RL-tuning process and achieving state-of-the-art performance with minimal compute resources. It includes codebase, models, and paper related to R1-Zero training implemented with the Oat framework, emphasizing research-friendly and efficient LLM RL techniques.

This repository provides a hands-on approach to Retrieval-Augmented Generation (RAG) techniques, simplifying advanced concepts into understandable implementations using Python libraries like openai, numpy, and matplotlib. It offers a collection of Jupyter Notebooks with concise explanations, step-by-step implementations, code examples, evaluations, and visualizations for various RAG techniques. The goal is to make RAG more accessible and demystify its workings for educational purposes.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

The UvA Deep Learning Tutorials repository contains a series of Jupyter notebooks designed to help understand theoretical concepts from lectures by providing corresponding implementations. The notebooks cover topics such as optimization techniques, transformers, graph neural networks, and more. They aim to teach details of the PyTorch framework, including PyTorch Lightning, with alternative translations to JAX+Flax. The tutorials are integrated as official tutorials of PyTorch Lightning and are relevant for graded assignments and exams.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

SploitCraft is a curated collection of security exploits, penetration testing techniques, and vulnerability demonstrations intended to help professionals and enthusiasts understand and demonstrate the latest in cybersecurity threats and offensive techniques. The repository is organized into folders based on specific topics, each containing directories and detailed READMEs with step-by-step instructions. Contributions from the community are welcome, with a focus on adding new proof of concepts or expanding existing ones while adhering to the current structure and format of the repository.

The mslearn-knowledge-mining repository contains lab files for Azure AI Knowledge Mining modules. It provides resources for learning and implementing knowledge mining techniques using Azure AI services. The repository is designed to help users explore and understand how to leverage AI for knowledge mining purposes within the Azure ecosystem.

Generative AI Indepth Basic to Advance is a repository focused on providing tutorials and resources related to generative artificial intelligence. The repository covers a wide range of topics from basic concepts to advanced techniques in the field of generative AI. Users can find detailed explanations, code examples, and practical demonstrations to help them understand and implement generative AI algorithms. The goal of this repository is to help beginners get started with generative AI and to provide valuable insights for more experienced practitioners.

This tutorial provides a comprehensive guide on building a small Large Language Model (LLM) from scratch using PyTorch. The author shares insights and experiences gained from working on LLM projects in the industry, aiming to help beginners understand the fundamental components of LLMs and training fine-tuning codes. The tutorial covers topics such as model structure overview, attention modules, optimization techniques, normalization layers, tokenizers, pretraining, and fine-tuning with dialogue data. It also addresses specific industry-related challenges and explores cutting-edge model concepts like DeepSeek network structure, causal attention, dynamic-to-static tensor conversion for ONNX inference, and performance optimizations for NPU chips. The series emphasizes hands-on practice with small models to enable local execution and plans to expand into multimodal language models and tensor parallel multi-card deployment.

This repository contains materials for the Microsoft Learn AI Fundamentals module. It covers the basics of artificial intelligence, machine learning, and data science. The content includes hands-on labs, interactive learning modules, and assessments to help learners understand key concepts and techniques in AI. Whether you are new to AI or looking to expand your knowledge, this module provides a comprehensive introduction to the fundamentals of AI.

This repository contains code for the O'Reilly Live Online Training for Deploying GPT & LLMs. Learn how to use GPT-4, ChatGPT, OpenAI embeddings, and other large language models to build applications for experimenting and production. Gain practical experience in building applications like text generation, summarization, question answering, and more. Explore alternative generative models such as Cohere and GPT-J. Understand prompt engineering, context stuffing, and few-shot learning to maximize the potential of GPT-like models. Focus on deploying models in production with best practices and debugging techniques. By the end of the training, you will have the skills to start building applications with GPT and other large language models.

AI Goat is a tool designed to help users learn about AI security through a series of vulnerable LLM CTF challenges. It allows users to run everything locally on their system without the need for sign-ups or cloud fees. The tool focuses on exploring security risks associated with large language models (LLMs) like ChatGPT, providing practical experience for security researchers to understand vulnerabilities and exploitation techniques. AI Goat uses the Vicuna LLM, derived from Meta's LLaMA and ChatGPT's response data, to create challenges that involve prompt injections, insecure output handling, and other LLM security threats. The tool also includes a prebuilt Docker image, ai-base, containing all necessary libraries to run the LLM and challenges, along with an optional CTFd container for challenge management and flag submission.

LangChain-Udemy-Course is a comprehensive course directory focusing on LangChain, a framework for generative AI applications. The course covers various aspects such as OpenAI API usage, prompt templates, Chains exploration, callback functions, memory techniques, RAG implementation, autonomous agents, hybrid search, LangSmith utilization, microservice architecture, and LangChain Expression Language. Learners gain theoretical knowledge and practical insights to understand and apply LangChain effectively in generative AI scenarios.

This project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

The LLM RAG Workshop repository provides a workshop on using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to generate and understand text in a human-like manner. It includes instructions on setting up the environment, indexing Zoomcamp FAQ documents, creating a Q&A system, and using OpenAI for generation based on retrieved information. The repository focuses on enhancing language model responses with retrieved information from external sources, such as document databases or search engines, to improve factual accuracy and relevance of generated text.

This repository serves as a comprehensive survey on the application of Large Language Models (LLMs) on tabular data, focusing on tasks such as prediction, data generation, and table understanding. It aims to consolidate recent progress in this field by summarizing key techniques, metrics, datasets, models, and optimization approaches. The survey identifies strengths, limitations, unexplored territories, and gaps in the existing literature, providing insights for future research directions. It also offers code and dataset references to empower readers with the necessary tools and knowledge to address challenges in this rapidly evolving domain.

I share hacking histories, not methods.

I'm Back Propagation, here to help you understand and apply back propagation techniques to your AI models.

Agente especializado en Logoterapia y Viktor Frankl, informativo y respetuoso.

Specializing in dopamine's impact on mental health, addiction, and motivation.

British cryptologist, innovating and explaining cryptography with wit.

Feeling overwhelmed? Burdened by stress? EmpathAI, your AI companion, understands. It listens without judgment, offering tools for managing anxiety, boosting mood, and building resilience. Find personalized support, relaxation techniques, and uplifting music all in one safe space.

I explain complex topics in simple, fun ways, like you're 5!

Helps understand North Andover's wetland regulations

Understand consent requirements and use of the easyConsent app.

This GPT helps you understand and apply the MITRE ATT&CK Framework, whether you are familiar with the concepts or not.

Understand the correct credit card to use to maximize your reward points.

Helps you understand those confusing ass old dudes and dudettes

A GPT that explains research methods in a language that everyone can easily understand.

Our aim is to understand your unique needs intimately, providing customized commendations that sincerely convey your appreciation and recognition. Moreover, we will design and match the most suitable images to accompany the sentiment of your praise, enhancing the impact visually.

An easy to understand wizard that guides you through questions about how to protect, back up and inherit essential digital information and assets such as crypto seed phrases, private keys, digital art, wallets, IDs, health and insurance information for you and your family.

i help u understand the lingo & the latest memes.

Guide to create and understand GPTs, with latest insights and practical tips.

Easy-to-understand guidance for non-profits in crafting impactful press releases.

Helps beginners understand DirectX 12 concepts and terminology

Helps beginners understand Vulkan concepts and terminology