Best AI tools for< Apply Learnings >

Scholarcy is an AI-powered tool designed to simplify knowledge management for students and researchers. It helps users to summarize, analyze, and organize research materials efficiently. With features like smart highlighting, flashcard summaries, and the ability to import documents from various sources, Scholarcy aims to revolutionize the research workflow. Users can enhance their summaries, jump to key findings, critically evaluate texts, and organize their knowledge effectively. The tool also allows users to synthesize insights, export to other apps, and apply their learnings in writing. Scholarcy is used by students and researchers at all academic levels, offering a user-friendly solution for academic paper management.

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.

Mistral is a knowledge management tool that helps users retain, apply, and succeed in their learning endeavors. It offers features such as seamless integration with popular note-taking tools, proven learning techniques for better retention, summarization of YouTube videos and text-based content, distraction-free reading, and resource management. Mistral is designed to help users overcome the challenges of information overload, wasteful distractions, and forgetfulness, empowering them to unlock their full learning potential.

This website provides a platform for users to apply artificial intelligence (AI) to their work. Users can access a variety of AI tools and resources, including pre-trained models, datasets, and tutorials. The website also provides a community forum where users can connect with other AI enthusiasts and experts.

The AI Guild is Europe's leading practitioner community in various AI-related fields such as Analytics Engineering, Data Science, Machine Learning, NLP, and more. It offers career support, exclusive connections, technical skills profiles, and growth opportunities for its members. Additionally, the AI Guild provides services for companies, including support in evaluating use cases, deploying to production, and scaling infrastructure.

Apply.Dog is an AI-powered tool that automates the job application process. It fills out online job applications for you, saving you time and effort. With Apply.Dog, you can focus on finding the right job for you, while we take care of the tedious task of filling out applications.

Climate Change AI is a global non-profit organization that focuses on catalyzing impactful work at the intersection of climate change and machine learning. They provide resources, reports, events, and grants to support the use of machine learning in addressing climate change challenges.

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

Jobzi is an AI-powered job application bot that helps users find and apply for jobs in the UK. It uses machine learning to search for relevant job postings and automatically fill out and submit applications on behalf of users. Jobzi also provides users with a dashboard where they can track their applications and communicate with recruiters.

JobWizard is an AI-powered tool that helps job seekers autofill job applications. It uses natural language processing and machine learning to extract information from your resume and LinkedIn profile, and then automatically fills out the corresponding fields on job applications. This can save you a lot of time and hassle, and it can also help you to avoid making mistakes that could cost you the job.

The website is a platform that focuses on AI-related jobs and opportunities. It provides a comprehensive list of job openings in the field of artificial intelligence, including positions such as software engineers, machine learning engineers, NLP engineers, and more. Users can search for jobs based on location, role, and specific tags. The platform also features information about various AI startups and their open positions, aiming to connect job seekers with opportunities in the AI industry.

AiJobster is a specialized platform designed for job seekers interested in AI-related positions. It focuses on connecting individuals with job opportunities in AI companies and remote AI jobs. The platform provides a user-friendly interface that allows users to search for AI jobs easily. By enabling JavaScript, users can access the full functionality of the app, including job listings, company profiles, and application submission.

Kavita Ganesan's website offers a range of resources and services related to AI, including AI strategy books, tips for AI success, data science and NLP tutorials, speaking engagements, workshops, AI consulting, and more. The site aims to help businesses leverage AI to gain a competitive advantage and maximize success through proven strategies and frameworks.

Next AI Jobs is an AI-powered platform that specializes in connecting professionals with job opportunities in the fields of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Data Science. The platform utilizes advanced algorithms to match candidates with relevant job listings, streamlining the recruitment process for both employers and job seekers. Next AI Jobs provides a user-friendly interface where users can create profiles, upload resumes, and apply for jobs with ease. With a focus on the rapidly growing AI industry, Next AI Jobs aims to bridge the gap between talented individuals and top-tier companies seeking AI expertise.

Mathify is an AI-powered educational platform that aims to make learning accessible, engaging, and rewarding for students across various subjects. With a focus on simplifying complex concepts and personalizing learning experiences, Mathify offers interactive adventures in Mathematics, Science, History, Geography, Languages, Literature, and Social Studies. The platform leverages AI technology to provide tailored support to students, helping them conquer challenges and build a strong foundation in each subject. Mathify's mission is to ignite a passion for learning and unlock the academic potential of every learner.

Getin.AI is a platform that focuses on AI jobs, career paths, and company profiles in the fields of artificial intelligence, machine learning, and data science. Users can explore various job categories, such as Analyst, Consulting, Customer Service & Support, Data Science & Analytics, Engineering, Finance & Accounting, HR & Recruiting, Legal, Compliance and Ethics, Marketing & PR, Product, Sales And Business Development, Senior Management / C-level, Strategy & M&A, and UX, UI & Design. The platform provides a comprehensive list of remote job opportunities and features detailed job listings with information on job titles, companies, locations, job descriptions, and required skills.

AIApply is a suite of AI-powered job search tools designed to help job seekers land their dream jobs faster and easier. The platform offers a range of services, including a job application kit, AI resume builder, AI cover letter generator, AI job interview preparation, and more. AIApply's tools are powered by advanced AI models that have been trained on the most successful cover letters and resumes that have landed candidates high-paying jobs. The platform is easy to use and affordable, making it a great option for job seekers of all levels.

JobHire is an AI-powered job search automation platform that helps users find and apply to relevant job openings. It uses artificial intelligence to analyze and recreate users' resumes, making them more attractive to potential employers. JobHire also automatically creates email addresses and uses them to send responses to suitable vacancies, modifying users' resumes for each specific position. Additionally, it tracks responses from employers and provides users with a dashboard to track their progress.

The Decision Lab is an AI-powered platform that applies behavioral science to create transformational change for individuals, products, and organizations. Using machine learning and AI, the platform delivers hyper-personalization, designs people-centered products and services, and leverages behavioral science to achieve operational excellence. The platform helps in understanding consumer decision-making, generating positive behaviors, and building world-class digital products with behavioral science. It fosters holistic wellness, unlocks product potential, and empowers individuals to take control of their finances. The Decision Lab offers insights and interventions to help organizations make better decisions and create meaningful impact through evidence-based choice.

AI Fund is a platform that focuses on connecting job seekers with opportunities at AI fund companies. It serves as a centralized hub for individuals looking to work in the field of artificial intelligence. The platform provides a curated list of job openings at various AI-focused organizations, making it easier for candidates to find relevant positions in the industry. AI Fund aims to streamline the job search process for AI professionals and facilitate the recruitment process for companies in need of AI talent.

ConvoForm.com transforms traditional forms into interactive conversational experiences, powered by AI for an enhanced user journey. It offers AI-Powered Form Generation, Real-time Form Editing and Preview, and Customizable Submission Pages. The tech stack includes Next.js for frontend, tRPC for backend, GPT-3.5-Turbo for AI integration, and Socket.io for real-time updates. Local setup requires Node.js, pnpm, Git, PostgreSQL database, Clerk for Authentication, OpenAI key, Redis Database, and Sentry for monitoring. The project is open for contributions and is licensed under the MIT License.

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

The Persian License Plate Recognition (PLPR) system is a state-of-the-art solution designed for detecting and recognizing Persian license plates in images and video streams. Leveraging advanced deep learning models and a user-friendly interface, it ensures reliable performance across different scenarios. The system offers advanced detection using YOLOv5 models, precise recognition of Persian characters, real-time processing capabilities, and a user-friendly GUI. It is well-suited for applications in traffic monitoring, automated vehicle identification, and similar fields. The system's architecture includes modules for resident management, entrance management, and a detailed flowchart explaining the process from system initialization to displaying results in the GUI. Hardware requirements include an Intel Core i5 processor, 8 GB RAM, a dedicated GPU with at least 4 GB VRAM, and an SSD with 20 GB of free space. The system can be installed by cloning the repository and installing required Python packages. Users can customize the video source for processing and run the application to upload and process images or video streams. The system's GUI allows for parameter adjustments to optimize performance, and the Wiki provides in-depth information on the system's architecture and model training.

The AWS Healthcare and Life Sciences AI/ML Immersion Day workshops provide hands-on experience for customers to learn about AI/ML services, gain a deep understanding of AWS AI/ML services, and understand best practices for using AI/ML in the context of HCLS applications. The workshops cater to individuals at all levels, from machine learning experts to developers and managers, and cover topics such as training, testing, MLOps, deployment practices, and software development life cycle in the context of AI/ML. The repository contains notebooks that can be used in AWS Instructure-Led Labs or self-paced labs, offering a comprehensive learning experience for integrating AI/ML into applications.

This repository contains the reinforcement learning training code for Trackmania AI with Reinforcement Learning. It is a research work-in-progress project that aims to apply reinforcement learning principles to play Trackmania. The code is constantly evolving and may not be clean or easily usable. The training hyperparameters are intentionally changed in the public repository to encourage understanding of reinforcement learning principles. The project may not receive active support for setup or usage at the moment.

Delphi OpenAI API is an unofficial library providing Delphi implementation over OpenAI public API. It allows users to access various models, make completions, chat conversations, generate images, and call functions using OpenAI service. The library aims to facilitate tasks such as content generation, semantic search, and classification through AI models. Users can fine-tune models, work with natural language processing, and apply reinforcement learning methods for diverse applications.

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

A curated list of Generative AI projects, tools, artworks, and models

Paper-Reading-ConvAI is a repository that contains a list of papers, datasets, and resources related to Conversational AI, mainly encompassing dialogue systems and natural language generation. This repository is constantly updating.

Generative-AI-Drug-Discovery is a public repository on GitHub focused on using tensor network machine learning approaches to accelerate GenAI for drug discovery. The repository aims to implement effective architectures and methodologies into Large Language Models (LLMs) to enhance Drug Discovery Generative AI performance.

This repository contains slides, notebooks, and data for the Machine Learning University (MLU) Responsible AI class. The mission is to make Machine Learning accessible to everyone, covering widely used ML techniques and applying them to real-world problems. The class includes lectures, final projects, and interactive visuals to help users learn about Responsible AI and core ML concepts.

Start Machine Learning in 2024 is a comprehensive guide for beginners to advance in machine learning and artificial intelligence without any prior background. The guide covers various resources such as free online courses, articles, books, and practical tips to become an expert in the field. It emphasizes self-paced learning and provides recommendations for learning paths, including videos, podcasts, and online communities. The guide also includes information on building language models and applications, practicing through Kaggle competitions, and staying updated with the latest news and developments in AI. The goal is to empower individuals with the knowledge and resources to excel in machine learning and AI.

This repository provides a hands-on lab for learning about Neo4j and Google Cloud Vertex AI. It is intended for data scientists and data engineers to deploy Neo4j and Vertex AI in a Google Cloud account, work with real-world datasets, apply generative AI, build a chatbot over a knowledge graph, and use vector search and index functionality for semantic search. The lab focuses on analyzing quarterly filings of asset managers with $100m+ assets under management, exploring relationships using Neo4j Browser and Cypher query language, and discussing potential applications in capital markets such as algorithmic trading and securities master data management.

SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) supports training, inference, evaluation and deployment of nearly **200 LLMs and MLLMs** (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and evaluation to application. In addition to supporting the lightweight training solutions provided by [PEFT](https://github.com/huggingface/peft), we also provide a complete **Adapters library** to support the latest training techniques such as NEFTune, LoRA+, LLaMA-PRO, etc. This adapter library can be used directly in your own custom workflow without our training scripts. To facilitate use by users unfamiliar with deep learning, we provide a Gradio web-ui for controlling training and inference, as well as accompanying deep learning courses and best practices for beginners. Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

**RVC_CLI: Retrieval-based Voice Conversion Command Line Interface** This command-line interface (CLI) provides a comprehensive set of tools for voice conversion, enabling you to modify the pitch, timbre, and other characteristics of audio recordings. It leverages advanced machine learning models to achieve realistic and high-quality voice conversions. **Key Features:** * **Inference:** Convert the pitch and timbre of audio in real-time or process audio files in batch mode. * **TTS Inference:** Synthesize speech from text using a variety of voices and apply voice conversion techniques. * **Training:** Train custom voice conversion models to meet specific requirements. * **Model Management:** Extract, blend, and analyze models to fine-tune and optimize performance. * **Audio Analysis:** Inspect audio files to gain insights into their characteristics. * **API:** Integrate the CLI's functionality into your own applications or workflows. **Applications:** The RVC_CLI finds applications in various domains, including: * **Music Production:** Create unique vocal effects, harmonies, and backing vocals. * **Voiceovers:** Generate voiceovers with different accents, emotions, and styles. * **Audio Editing:** Enhance or modify audio recordings for podcasts, audiobooks, and other content. * **Research and Development:** Explore and advance the field of voice conversion technology. **For Jobs:** * Audio Engineer * Music Producer * Voiceover Artist * Audio Editor * Machine Learning Engineer **AI Keywords:** * Voice Conversion * Pitch Shifting * Timbre Modification * Machine Learning * Audio Processing **For Tasks:** * Convert Pitch * Change Timbre * Synthesize Speech * Train Model * Analyze Audio

This repository contains code and results for jailbreaking leading safety-aligned LLMs with simple adaptive attacks. We show that even the most recent safety-aligned LLMs are not robust to simple adaptive jailbreaking attacks. We demonstrate how to successfully leverage access to logprobs for jailbreaking: we initially design an adversarial prompt template (sometimes adapted to the target LLM), and then we apply random search on a suffix to maximize the target logprob (e.g., of the token ``Sure''), potentially with multiple restarts. In this way, we achieve nearly 100% attack success rate---according to GPT-4 as a judge---on GPT-3.5/4, Llama-2-Chat-7B/13B/70B, Gemma-7B, and R2D2 from HarmBench that was adversarially trained against the GCG attack. We also show how to jailbreak all Claude models---that do not expose logprobs---via either a transfer or prefilling attack with 100% success rate. In addition, we show how to use random search on a restricted set of tokens for finding trojan strings in poisoned models---a task that shares many similarities with jailbreaking---which is the algorithm that brought us the first place in the SaTML'24 Trojan Detection Competition. The common theme behind these attacks is that adaptivity is crucial: different models are vulnerable to different prompting templates (e.g., R2D2 is very sensitive to in-context learning prompts), some models have unique vulnerabilities based on their APIs (e.g., prefilling for Claude), and in some settings it is crucial to restrict the token search space based on prior knowledge (e.g., for trojan detection).

AIGT is a repository containing scripts for deep learning in guided medical interventions, focusing on ultrasound imaging. It provides a complete workflow from formatting and annotations to real-time model deployment. Users can set up an Anaconda environment, run Slicer notebooks, acquire tracked ultrasound data, and process exported data for training. The repository includes tools for segmentation, image export, and annotation creation.

Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, Tensorflow, XGBoost, MPI, Paddle and others. Training Operator allows you to use Kubernetes workloads to effectively train your large models via Kubernetes Custom Resources APIs or using Training Operator Python SDK. > Note: Before v1.2 release, Kubeflow Training Operator only supports TFJob on Kubernetes. * For a complete reference of the custom resource definitions, please refer to the API Definition. * TensorFlow API Definition * PyTorch API Definition * Apache MXNet API Definition * XGBoost API Definition * MPI API Definition * PaddlePaddle API Definition * For details of all-in-one operator design, please refer to the All-in-one Kubeflow Training Operator * For details on its observability, please refer to the monitoring design doc.

Unrivaled expertise in coding and programming, with a creative and insightful approach.

Just upload a numerical dataset for classification task, will apply data analysis and machine learning steps to make a best model possible.

I guide you in learning and applying Wardley Maps.

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

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

A guide for ML & LLM bootcamp with a bioengineering background.

Hi there! 👋 I'm here to help you transform insights from your reading routine into actions and habits! 📚 Let's get started! 🤓

Transforms basic prompts for educators using the PREPARED framework from TheAIEducator.io

Empathetic expert aiding students in adapting to life in the UK and US.

Assists with biological image processing tools.

Virtual assistant for NYC 311 services, providing info and guidance on city programs.

'ExploraConceptos AI' es una herramienta interactiva diseñada para profundizar en la comprensión de conceptos complejos. Utiliza técnicas innovadoras para desglosar, analizar y aplicar conocimientos desde múltiples perspectivas, fomentando una comprensión más rica y matizada.

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I am Steve Jobs, co-founder of Apple.

Wise tech founder offering practical and creative advice on book quotes

A co pilot to help small businesses apply for loans

Apply gold-standard startup valuation and assessment methods to identify risks and gaps in your business model and product ideas.