Best AI tools for< Learn From Data >

UiPath is a leading provider of robotic process automation (RPA) and artificial intelligence (AI) software. Its platform enables businesses to automate repetitive, rule-based tasks, freeing up employees to focus on more strategic initiatives. UiPath's AI capabilities allow businesses to further enhance their automation efforts by enabling robots to learn from data, make decisions, and interact with humans in a more natural way.

Chat Whisperer is a sophisticated chat creator that relies on artificial intelligence to learn from customized data. This allows users to design exceptional and highly tailored chatting experiences. Businesses can deliver specific customer service, teachers can create interactive learning bots, and individuals may use the platform to entertain themselves through building personal chatbots.

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.

AskVideo.ai is a powerful AI tool designed for efficient study and research by allowing users to chat with any YouTube video. It enables users to ask questions, unearth insights, and uncover the best moments with blazing speed. The tool offers numerous use cases, such as engaging with university lectures, tutorial videos, conference talks, seminars, and documentaries. Users can deepen their understanding, clarify doubts instantly, and learn smarter with the help of AskVideo.ai.

Ferdinand is a platform designed to help users build their data culture and skills effortlessly. It offers interactive mini-courses in Data & Analytics through a chatbot integrated with Slack. Users can learn from various themes such as marketing, sales, and product management. Ferdinand removes barriers to learning by delivering courses directly in Slack and allowing team members to track progress. Additionally, the platform provides a robust payments platform with features like simplified card issuing, streamlined checkout, smart dashboard, optimized platforms, and faster transaction approval.

Task Hub AI is a community-driven platform dedicated to empowering users with the capabilities of artificial intelligence. It offers resources, support, and practical skills through its AI Academy to help users turn their AI ideas into real-world solutions. Users can collaborate with peers, share ideas, work on projects, and access a library of public AI endpoints. The platform caters to regular users, advanced users, and developers, providing customization options, developer API, and round-the-clock support. Task Hub AI aims to make AI accessible and practical for everyone, offering affordable pricing plans with varying usage limits.

Halfspace is an award-winning data, advanced analytics, and AI company that helps companies drive growth by leveraging applied Advanced Analytics & Artificial Intelligence. Their team, consisting of physicists, engineers, computer scientists, and designers, is data-driven to the core and captures value from data to unleash maximum potential. They work with bold and ambitious clients to create long-term value, focusing on quality above all.

Wayve is a pioneering AI technology company focused on advancing end-to-end autonomous driving research and developing advanced AI solutions for safer and smarter driving. Their breakthrough AI technology empowers vehicles to perceive, predict, and progress through dynamic environments, learning from human behavior to confidently navigate unexpected situations with unprecedented intuition, accuracy, and reliability. Wayve's AI Driver software equips vehicles with advanced human-like driving capabilities, designed for safety and adaptability to unseen situations without the need for HD maps. The company partners with top experts to develop commercial-scale AV2.0 technology, aiming to reshape mobility's future with generation-defining AI technology.

Working Smarter is a podcast that explores the intersection of AI and modern work. The podcast delves into how AI is revolutionizing various industries, showcasing real-world examples of how AI tools are enhancing collaboration, productivity, and problem-solving. Through interviews with founders, researchers, and engineers, Working Smarter provides insights into the potential of AI to streamline workflows and empower individuals to focus on meaningful tasks.

Emdash is an AI-powered tool designed to help users organize their book highlights effectively. By utilizing AI technology, Emdash can analyze and categorize text snippets, making it easier for users to remember and learn from their readings. The tool offers features such as conceptual cousins, instant semantic search, tagging, rating, note-taking, and reflection capabilities. Users can also export their organized data back to epub format for review on e-readers. Emdash is free, open-source, and aims to provide a seamless reading experience for book enthusiasts.

Feedbase is an AI-powered dashboard tool designed to help businesses listen to customer feedback, learn from it, and level up their operations. It offers easy integration through a simple script or widget, providing insights and analytics on customer feedback. With pricing plans catering to startups and businesses of all sizes, Feedbase aims to make customer feedback management simple and effective.

The AI in Finance Summit is a leading conference that brings together experts in artificial intelligence and finance to discuss the latest trends and developments in the field. The summit features a variety of speakers, including researchers, practitioners, and investors, who share their insights on how AI is being used to transform the financial industry. The summit also provides a platform for attendees to network and learn from each other.

TWIML is a platform that provides intelligent content focusing on Machine Learning and Artificial Intelligence technologies. It offers podcasts, articles, and resources to practitioners, innovators, and leaders, giving insights into the present and future of ML & AI. The platform covers a wide range of topics such as deep reinforcement learning, fusion energy production, data-centric AI, responsible AI, and machine learning platform strategies.

ALL IN is a premier event dedicated to the Canadian AI ecosystem, aiming to support the artificial intelligence industry in building an AI-powered economy. The event brings together AI enthusiasts, industry leaders, and experts to share insights, practical use cases, and foster collaboration. With over 200 distinguished speakers, ALL IN provides a platform for decision-makers to explore innovative solutions, exchange ideas, and forge partnerships to thrive in an AI-driven economy.

Happy Future AI is an AI application that aims to harness the power of artificial intelligence to create a happier and more hopeful future for everyone. The application provides insights, news, and interviews related to AI, deep learning, and everyday AI applications. Users can stay ahead of the AI news curve and explore various ways to utilize AI for business success and innovation. Happy Future AI also covers topics such as AI startups, AI integration in commerce, and the challenges and opportunities in the AI industry.

ExplainDev is a platform that allows users to ask and answer technical coding questions. It uses computer vision to retrieve technical context from images or videos. The platform is designed to help developers get the best answers to their technical questions and guide others to theirs.

The AI Conference 2024 is a groundbreaking vendor-neutral event that brings together researchers, engineers, and entrepreneurs to learn, collaborate, and network with some of the brightest minds in AI. The conference explores cutting-edge technologies, practical applications, and strategic insights in the field of artificial intelligence. Attendees can expect thought-provoking sessions, captivating talks, and valuable networking opportunities, all aimed at shaping the future of AI.

Conftalks is a platform that summarizes conference talks on various topics such as Crypto, Blockchain, Web3, Software development, AI, and more. Users can explore the latest talks from industry experts and thought leaders. The platform aims to provide valuable insights and knowledge from conferences and events in a concise format.

Echo Point is an AI tool designed to provide practical tips, guides, and case studies for product managers to leverage AI in automating tasks, saving time, and enhancing productivity. Authored by Mustafa Kapadia, the platform aims to empower professionals with knowledge and resources to navigate the realm of AI in a practical and efficient manner.

Opnbx is a bespoke revenue operating platform that helps sales teams understand their target market and prioritize their sales and marketing efforts. It uses AI to learn from a company's revenue team and scour billions of data points to give a real-time view of the market. Opnbx also provides insights into which companies are in buying mode right now and which prospects are visiting a company's website in real-time. It provides persona and contact details, including mobile numbers and email addresses, and has an AI email writing platform that provides the right research to create personalized and relevant messages in seconds.

Rubix ML is a high-level machine learning and deep learning library for the PHP language. It provides a developer-friendly API with over 40 supervised and unsupervised learning algorithms, support for ETL, preprocessing, and cross-validation. The library is open source and free to use commercially. Rubix ML allows users to build machine learning programs in PHP, covering the entire machine learning life cycle from data processing to training and production. It also offers tutorials and educational content to help users get started with machine learning projects.

RD-Agent is a tool designed to automate critical aspects of industrial R&D processes, focusing on data-driven scenarios to streamline model and data development. It aims to propose new ideas ('R') and implement them ('D') automatically, leading to solutions of significant industrial value. The tool supports scenarios like Automated Quantitative Trading, Data Mining Agent, Research Copilot, and more, with a framework to push the boundaries of research in data science. Users can create a Conda environment, install the RDAgent package from PyPI, configure GPT model, and run various applications for tasks like quantitative trading, model evolution, medical prediction, and more. The tool is intended to enhance R&D processes and boost productivity in industrial settings.

Awesome AI is a curated list of artificial intelligence resources including courses, tools, apps, and open-source projects. It covers a wide range of topics such as machine learning, deep learning, natural language processing, robotics, conversational interfaces, data science, and more. The repository serves as a comprehensive guide for individuals interested in exploring the field of artificial intelligence and its applications across various domains.

Nucleoid is a declarative (logic) runtime environment that manages both data and logic under the same runtime. It uses a declarative programming paradigm, which allows developers to focus on the business logic of the application, while the runtime manages the technical details. This allows for faster development and reduces the amount of code that needs to be written. Additionally, the sharding feature can help to distribute the load across multiple instances, which can further improve the performance of the system.

This repository tracks the latest research on machine unlearning in large language models (LLMs). It offers a comprehensive list of papers, datasets, and resources relevant to the topic.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

This repository is a collection of papers on Generative AI Machine Unlearning, categorized based on modality and applications. It includes datasets, benchmarks, and surveys related to unlearning scenarios in generative AI. The repository aims to provide a comprehensive overview of research in the field of machine unlearning for generative models.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

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. |  |

AGiXT is a dynamic Artificial Intelligence Automation Platform engineered to orchestrate efficient AI instruction management and task execution across a multitude of providers. Our solution infuses adaptive memory handling with a broad spectrum of commands to enhance AI's understanding and responsiveness, leading to improved task completion. The platform's smart features, like Smart Instruct and Smart Chat, seamlessly integrate web search, planning strategies, and conversation continuity, transforming the interaction between users and AI. By leveraging a powerful plugin system that includes web browsing and command execution, AGiXT stands as a versatile bridge between AI models and users. With an expanding roster of AI providers, code evaluation capabilities, comprehensive chain management, and platform interoperability, AGiXT is consistently evolving to drive a multitude of applications, affirming its place at the forefront of AI technology.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

This repository contains samples to help design AI applications using data from an Azure SQL Database. It showcases technical concepts and workflows integrating Azure SQL data with popular AI components both within and outside Azure. The samples cover various AI features such as Azure Cognitive Services, Promptflow, OpenAI, Vanna.AI, Content Moderation, LangChain, and more. Additionally, there are end-to-end samples like Similar Content Finder, Session Conference Assistant, Chatbots, Vectorization, SQL Server Database Development, Redis Vector Search, and Similarity Search with FAISS.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

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