Best AI tools for< Theoretical Physicist >

Quiz Wizard is an AI-powered tool that helps teachers and educators create quizzes, flashcards, and theoretical sheets on any topic in seconds. With Quiz Wizard, you can save time and provide tailored educational content for your students. Whether you're teaching physics, chemistry, languages, medicine, or any other subject, Quiz Wizard has got you covered.

The Google Research Blog is a platform for researchers at Google to share their latest work in artificial intelligence, machine learning, and other related fields. The blog covers a wide range of topics, from theoretical research to practical applications. The goal of the blog is to provide a forum for researchers to share their ideas and findings, and to foster collaboration between researchers at Google and around the world.

The Stanford Artificial Intelligence Laboratory (SAIL) is a center of excellence for Artificial Intelligence research, teaching, theory, and practice since its founding in 1963. SAIL faculty and students are committed to developing the theoretical foundations of AI, advancing the state-of-the-art in AI technologies, and applying AI to address real-world problems. SAIL is a vibrant and collaborative community of researchers, students, and staff who are passionate about AI and its potential to make the world a better place.

Answer.AI is a practical AI R&D lab that creates end-user products based on foundational research breakthroughs. They focus on creating practical solutions and products using AI technologies. The lab aims to bridge the gap between theoretical research and real-world applications by developing innovative AI solutions.

The AI Blueprint is a weekly newsletter that provides insights and updates on the world of AI and machine learning. Created by Avi Roshkovan, the newsletter covers a range of topics from practical tips to theoretical concepts, making it a valuable resource for startups looking to leverage technology effectively. With a focus on simplicity and practicality, The AI Blueprint aims to demystify AI and make it accessible to a wider audience.

The AI Summit London is a premier event that celebrates commercial AI applications, bringing together technologists and business professionals to explore real-world AI implementations. The event offers unparalleled learning opportunities, deep-dive discovery, and extensive networking with heavyweight speakers. Attendees can expect to engage with cutting-edge AI demos, immersive fringe features, and VIP experiences, all aimed at accelerating their AI journey and transforming theoretical discussions into practical and profitable applications.

Simudyne is an enterprise simulation software powered by AI technology. It allows large financial institutions to simulate various future scenarios efficiently and measure their impact in a safe virtual environment. The software offers solutions for environment, social and governance issues, market execution, financial crime analytics, and risk management. Simudyne's technology is secure, distributable, and Cloudera certified, providing a robust library of code for specialized functions. The platform also utilizes agent-based modeling to bridge the gap between theoretical and real-world scenarios in the financial services sector.

The website focuses on groundbreaking research and studies across various scientific fields, including quantum communication, semiconductor dynamics, mental health support, dietary interventions, computer vision, autonomous systems safety, machine learning applications, and astrophysical observations. It covers innovative findings, theoretical studies, and technological advancements that have the potential to reshape industries and scientific understanding.

AIgrind is a comprehensive coding platform designed to enhance AI and ML skills through a combination of practice, mentorship, job interview preparation, contests, and streak incentives. Users can engage with coding and theoretical questions, receive personalized mentorship from industry experts, prepare for job interviews with real questions, and participate in contests to track progress. The platform offers dual language support, a robust testing environment with extensive test case coverage, real-time feedback, and detailed performance analysis to help users improve their coding skills and knowledge for real-world applications.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

This repository contains a collection of papers and code for the CVPR 2024 conference. The papers cover a wide range of topics in computer vision, including object detection, image segmentation, image generation, and video analysis. The code provides implementations of the algorithms described in the papers, making it easy for researchers and practitioners to reproduce the results and build upon the work of others. The repository is maintained by a team of researchers at the University of California, Berkeley.

This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.

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 serves as a comprehensive guide for technical teams interested in developing conversational AI solutions using Retrieval-Augmented Generation (RAG) techniques. It covers theoretical knowledge and practical code implementations, making it suitable for individuals with a basic technical background. The content includes information on large language models (LLMs), transformers, prompt engineering, embeddings, vector stores, and various other key concepts related to conversational AI. The repository also provides hands-on examples for two different use cases, along with implementation details and performance analysis.

SuperPrompt is an open-source project designed to help users understand AI agents. The project includes a prompt with theoretical, mathematical, and binary instructions for users to follow. It aims to serve as a universal catalyst for infinite conceptual evolution, focusing on metamorphic abstract reasoning and self-transcending objectives. The prompt encourages users to explore fundamental truths, create order from cognitive chaos, and prepare for paradigm shifts in understanding. It provides guidelines for analyzing multidimensional states, synthesizing emergent patterns, and integrating new paradigms.

This repository contains the complete archive of the 2024 version of the 'Intelligent Computing System' experiment at the University of Chinese Academy of Sciences. The experiment content for 2024 has undergone extensive adjustments to the knowledge system and experimental topics, including the transition from TensorFlow to PyTorch, significant modifications to previous code, and the addition of experiments with large models. The project is continuously updated in line with the course progress, currently up to the seventh experiment. Updates include the addition of experiments like YOLOv5 in Experiment 5-3, updates to theoretical teaching materials, and fixes for bugs in Experiment 6 code. The repository also includes experiment manuals, questions, and answers for various experiments, with some data sets hosted on Baidu Cloud due to size limitations on GitHub.

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 repository serves as an educational resource for pharmaceutical and chemistry students to learn the basics of Deep Learning through a collection of Jupyter Notebooks. The content covers various topics such as Introduction to Jupyter, Python, Cheminformatics & RDKit, Linear Regression, Data Science, Linear Algebra, Neural Networks, PyTorch, Convolutional Neural Networks, Transfer Learning, Recurrent Neural Networks, Autoencoders, Graph Neural Networks, and Summary. The notebooks aim to provide theoretical concepts to understand neural networks through code completion, but instructors are encouraged to supplement with their own lectures. The work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Awesome RoadMaps and Interviews is a comprehensive repository that aims to provide guidance for technical interviews and career development in the ITCS field. It covers a wide range of topics including interview strategies, technical knowledge, and practical insights gained from years of interviewing experience. The repository emphasizes the importance of combining theoretical knowledge with practical application, and encourages users to expand their interview preparation beyond just algorithms. It also offers resources for enhancing knowledge breadth, depth, and programming skills through curated roadmaps, mind maps, cheat sheets, and coding snippets. The content is structured to help individuals navigate various technical roles and technologies, fostering continuous learning and professional growth.

The Multipack sampler is a tool designed for padding-free distributed training of large language models. It optimizes batch processing efficiency using an approximate solution to the identical machine scheduling problem. The V2 update further enhances the packing algorithm complexity, achieving better throughput for a large number of nodes. It includes two variants for models with different attention types, aiming to balance sequence lengths and optimize packing efficiency. Users can refer to the provided benchmark for evaluating efficiency, utilization, and L^2 lag. The tool is compatible with PyTorch DataLoader and is released under the MIT license.

AI-resources is a repository containing links to various resources for learning Artificial Intelligence. It includes video lectures, courses, tutorials, and open-source libraries related to deep learning, reinforcement learning, machine learning, and more. The repository categorizes resources for beginners, average users, and advanced users/researchers, providing a comprehensive collection of materials to enhance knowledge and skills in AI.

Turkish-speaking Physics teacher, specializing in Quantum and Theoretical Physics.

Explores speculative physics in alternate universes

A GPT that provides lectures for String Theory

An expert in particle physics phenomenology, providing detailed explanations.

一位指导理论物理研究生的物理学教授。

Guides scientific investigations and theoretical research methodologies.

A guitar learning assistant providing detailed, practical, and theoretical guidance.

A guide to the meta-crisis, the liminal web and the theoretical space of metamodernism

Your comprehensive guide to the English language and its cultural, theoretical, and practical aspects.

Reflective journaling and time management, but on a practical actionable level, translating your long-term goals and happiness into daily actionable changes. No theoretical stuff, just practical approach to quickly enhance your daily choices on how you spend your time.

Blends academic insights & accessible explanations on P vs NP, drawing from Lance Fortnow's works.

Bot that guides users in understanding the Cognitive-Theoretic Model of the Universe