Best AI tools for< Learning Coping Mechanisms >

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.

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.

Vize.ai is a custom image recognition API provided by Ximilar, a leading company in Visual AI and Search. The tool offers powerful artificial intelligence capabilities with high accuracy using deep learning algorithms. It allows users to easily set up and implement cutting-edge vision automation without any development costs. Vize.ai enables users to train custom neural networks to recognize specific images and provides a scalable solution with continuous improvements in machine learning algorithms. The tool features an intuitive interface that requires no machine learning or coding knowledge, making it accessible for a wide range of users across industries.

Ximilar Visual AI for Business is an AI tool that offers a comprehensive platform for image recognition and visual search solutions. It provides features such as image classification, regression, object detection, AI model combination, image annotation, and more. Users can easily build custom machine learning models without coding, access ready-to-use visual AI demos, and benefit from features like image upscaling, background removal, and color extraction. The platform caters to various industries including fashion, home decor, stock photos, collectibles, med & biotech, manufacturing, and real estate.

WPTurbo is an AI-powered tool designed for WordPress developers to generate code snippets quickly and efficiently. By leveraging artificial intelligence, WPTurbo automates the process of writing clean, customizable code for various WordPress projects, including custom plugins, theme customization, and website functionality enhancements. The tool offers a snippets library to store and manage code snippets, streamlining development cycles and ensuring uniformity in design and functionality. With features like Projects for organizing code projects and seamless integrations with the WordPress ecosystem, WPTurbo aims to empower developers to create high-quality websites with ease.

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.

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.

Pgrammer is an AI-powered platform designed to help users practice coding interview questions with hints and personalized learning experiences. Unlike traditional methods like LeetCode, Pgrammer offers a diverse set of coding challenges in over 20 programming languages, real-time hints, solution analysis, and feedback to improve users' skills and knowledge. The platform aims to provide a supportive environment for users to prepare for coding interviews with confidence.

Coddy is an AI-powered coding assistant that helps developers write better code faster. It provides real-time feedback, code completion, and error detection, making it the perfect tool for both beginners and experienced developers. Coddy also integrates with popular development tools like Visual Studio Code and GitHub, making it easy to use in your existing workflow.

Liner is a free and easy-to-use tool that allows users to train machine learning models without writing any code. It provides a user-friendly interface that guides users through the process of importing data, selecting a model, and training the model. Liner also offers a variety of pre-trained models that can be used for common tasks such as image classification, text classification, and object detection. With Liner, users can quickly and easily create and deploy machine learning applications without the need for specialized knowledge or expertise.

Codiga is a static code analysis tool that helps developers write clean, safe, and secure code. It works in real-time in your IDE and CI/CD pipelines, and it can be customized to meet your specific needs. Codiga supports a wide range of languages and frameworks, and it integrates with popular tools like GitHub, GitLab, and Bitbucket.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

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.

CodeComplete is an AI-powered coding assistant designed specifically for enterprise needs. It is efficient, reliable, and equipped with cutting-edge technology to improve developer productivity. CodeComplete offers a comprehensive suite of coding tools to improve end-to-end developer workflow, including code generation, code chat, automated unit test generation, automated documentation, and refactoring & migrations.

Interview Solver is a desktop application that acts as your copilot during coding interviews, providing instant solutions to LeetCode problems and system design questions. It features screengrabbing capabilities, one-shot solutions, query selected text functionality, global hotkeys, and syntax highlighting for all major languages. Interview Solver is designed to give you an AI advantage during live interviews, helping you land your dream job.

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.

SpellBox is a versatile AI coding assistant that helps developers of all levels write code faster and more efficiently. With SpellBox, you can say goodbye to hours of frustrating coding and hello to quick, easy solutions. SpellBox creates the code you need from simple prompts, so you can solve your toughest programming problems in seconds.

AICommit is an AI-powered programming assistant for JetBrains IDEs. It is based on OpenAI GPT and provides a range of intelligent coding features, including automated commit message generation, code optimization, code interpretation, documentation generation, code conversion, and translation. AICommit can help you make your coding process more efficient and convenient.

CodePal is a comprehensive platform that offers a range of coding helpers and tools to assist developers. It includes AI-powered code generators that can translate plain words into computer code, helping users automate tasks, improve code quality, and enhance productivity. CodePal supports various programming languages and technologies, making it a versatile tool for developers of all levels.

CursorLens is an open-source dashboard designed to provide insights for AI-assisted coding within the Cursor.sh IDE. It allows users to log AI code generations, track usage, and control AI models, including local ones. Users can run CursorLens locally or utilize the upcoming hosted version for enhanced convenience and efficiency.

This repository contains the code for coding, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch). In _Build a Large Language Model (From Scratch)_, you'll discover how LLMs work from the inside out. In this book, I'll guide you step by step through creating your own LLM, explaining each stage with clear text, diagrams, and examples. The method described in this book for training and developing your own small-but-functional model for educational purposes mirrors the approach used in creating large-scale foundational models such as those behind ChatGPT.

MachineSoM is a code repository for the paper 'Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View'. It focuses on the emergence of intelligence from collaborative and communicative computational modules, enabling effective completion of complex tasks. The repository includes code for societies of LLM agents with different traits, collaboration processes such as debate and self-reflection, and interaction strategies for determining when and with whom to interact. It provides a coding framework compatible with various inference services like Replicate, OpenAI, Dashscope, and Anyscale, supporting models like Qwen and GPT. Users can run experiments, evaluate results, and draw figures based on the paper's content, with available datasets for MMLU, Math, and Chess Move Validity.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

GLake is an acceleration library and utilities designed to optimize GPU memory management and IO transmission for AI large model training and inference. It addresses challenges such as GPU memory bottleneck and IO transmission bottleneck by providing efficient memory pooling, sharing, and tiering, as well as multi-path acceleration for CPU-GPU transmission. GLake is easy to use, open for extension, and focuses on improving training throughput, saving inference memory, and accelerating IO transmission. It offers features like memory fragmentation reduction, memory deduplication, and built-in security mechanisms for troubleshooting GPU memory issues.

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.

Awesome-LLM-Interpretability is a curated list of materials related to LLM (Large Language Models) interpretability, covering tutorials, code libraries, surveys, videos, papers, and blogs. It includes resources on transformer mechanistic interpretability, visualization, interventions, probing, fine-tuning, feature representation, learning dynamics, knowledge editing, hallucination detection, and redundancy analysis. The repository aims to provide a comprehensive overview of tools, techniques, and methods for understanding and interpreting the inner workings of large language models.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

Nixtla is a production-ready generative pretrained transformer for time series forecasting and anomaly detection. It can accurately predict various domains such as retail, electricity, finance, and IoT with just a few lines of code. TimeGPT introduces a paradigm shift with its standout performance, efficiency, and simplicity, making it accessible even to users with minimal coding experience. The model is based on self-attention and is independently trained on a vast time series dataset to minimize forecasting error. It offers features like zero-shot inference, fine-tuning, API access, adding exogenous variables, multiple series forecasting, custom loss function, cross-validation, prediction intervals, and handling irregular timestamps.

GenAI Agents is a comprehensive repository for developing and implementing Generative AI (GenAI) agents, ranging from simple conversational bots to complex multi-agent systems. It serves as a valuable resource for learning, building, and sharing GenAI agents, offering tutorials, implementations, and a platform for showcasing innovative agent creations. The repository covers a wide range of agent architectures and applications, providing step-by-step tutorials, ready-to-use implementations, and regular updates on advancements in GenAI technology.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

SharpNEAT is a complete implementation of NEAT written in C# and targeting .NET 9. It provides an implementation of an Evolutionary Algorithm (EA) with the specific goal of evolving a population of neural networks towards solving some goal problem task. The framework facilitates research into evolutionary computation and specifically evolution of neural networks, allowing for modular experimentation with genetic coding and evolutionary algorithms.

Llama-github is a powerful tool that helps retrieve relevant code snippets, issues, and repository information from GitHub based on queries. It empowers AI agents and developers to solve coding tasks efficiently. With features like intelligent GitHub retrieval, repository pool caching, LLM-powered question analysis, and comprehensive context generation, llama-github excels at providing valuable knowledge context for development needs. It supports asynchronous processing, flexible LLM integration, robust authentication options, and logging/error handling for smooth operations and troubleshooting. The vision is to seamlessly integrate with GitHub for AI-driven development solutions, while the roadmap focuses on empowering LLMs to automatically resolve complex coding tasks.

Gemini is an open-source model designed to handle multiple modalities such as text, audio, images, and videos. It utilizes a transformer architecture with special decoders for text and image generation. The model processes input sequences by transforming them into tokens and then decoding them to generate image outputs. Gemini differs from other models by directly feeding image embeddings into the transformer instead of using a visual transformer encoder. The model also includes a component called Codi for conditional generation. Gemini aims to effectively integrate image, audio, and video embeddings to enhance its performance.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

A collection of papers related to knowledge distillation of large language models (LLMs). The repository focuses on techniques to transfer advanced capabilities from proprietary LLMs to smaller models, compress open-source LLMs, and refine their performance. It covers various aspects of knowledge distillation, including algorithms, skill distillation, verticalization distillation in fields like law, medical & healthcare, finance, science, and miscellaneous domains. The repository provides a comprehensive overview of the research in the area of knowledge distillation of LLMs.

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. |  |

LongRoPE is a method to extend the context window of large language models (LLMs) beyond 2 million tokens. It identifies and exploits non-uniformities in positional embeddings to enable 8x context extension without fine-tuning. The method utilizes a progressive extension strategy with 256k fine-tuning to reach a 2048k context. It adjusts embeddings for shorter contexts to maintain performance within the original window size. LongRoPE has been shown to be effective in maintaining performance across various tasks from 4k to 2048k context lengths.

The 'llms' repository is a comprehensive guide on Large Language Models (LLMs), covering topics such as language modeling, applications of LLMs, statistical language modeling, neural language models, conditional language models, evaluation methods, transformer-based language models, practical LLMs like GPT and BERT, prompt engineering, fine-tuning LLMs, retrieval augmented generation, AI agents, and LLMs for computer vision. The repository provides detailed explanations, examples, and tools for working with LLMs.

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