AI Resource Guide : by @florist_notes
Artificial Intelligence is about mimicking the human senses to its truest forms:
💠 COMPUTER VISION < What we see
💠 NATURAL LANGUAGE PROCESSING < What we speak, read or hear
💠 REINFORCEMENT LEARNING < How we learn from experience
💠 AUGMENTED REALITY < Digital twin of the environment where we live
💠 ROBOTICS < What we can touch
💠 SENSOR FUSION & EDGE ELECTRONICS < How we feel, smell and taste
💠 MACHINE LEARNING < learn about the world around us
+ SUPERVISED LEARNING < give labels & information
+ UNSUPERVISED LEARNING < let ML algo find patterns itself
+ META LEARNING < learn about the learnings of the world around us
In addition:
💠 SYSTEMS and DATA CENTER < How we remember and make use of known tools
💠 MATHEMATICS < rule of nature that governs patterns and logic
Intro blogs : history of ai, intro to ai, introtodeeplearning, adeshpande (cnn), colah, intro to nlp, intro to rl, ros cv, robotics, a tour of ML algorithms, Machine Learning Algorithms & Models Explained with Python, awesome-ml-blogs, google:ml-crash-course.
DATA: Everything in CS is 0 or 1. Life has been binary! Most common data formats: Image / Video / Audio / Text / etc (other formats).
Mathematics: Linear Algebra | Tool: Python + PyTorch/ TensorFlow | Environment: Anaconda.
YOUTUBE CHANNELS: 2 minutes paper, StatQuest, 3blue1brown #some2, sentdex, Yannic Kilcher, Lex Fridman, stanfordonline, AI Explained, AI Grid, OpenAI, Google Deepmind, freeCodeCamp, WeightsBiases, Aladdin Persson, Patrick Loeber, Edan Meyer, Eye on AI, SerranoAcademy, Gabriel Mongaras.
resources : basic nn, neural networks - 3b1b, how are memories stored in neural network?, loss function, LF 2, Optimizers / adaptive learning rate (Gradient Descent, Adam, adagrad, adadelta, RMSProp etc), Activation Function, ML interview ques: article 1, article 2, article 3, ML design interview, article 5, 3b1b - But what is a GPT? Visual intro to transformers, Attention in transformers, visually explained, What Do Neural Networks Really Learn?, The moment we stopped understanding AI [AlexNet], The future of AI looks like THIS (& it can learn infinitely), Neural Networks: Zero to Hero, Watching Neural Networks Learn, Backpropagation from the ground up, The moment we stopped understanding AI [AlexNet], A Secret Weapon for Predicting Outcomes: The Binomial Distribution.
Into AI Courses: ML Specialization, Deep Learning Specialization, Mathematics for ML, Machine Learning Engineering for Production (MLOps) Specialization, DeepLearningAI courses.
|
100 page Machine Learning book (book) |
Neural Network Learning: Theoretical Foundations (book) |
Probabilistic Graphical Models: Principles and Techniques(book) |
Deep Learning (Adaptive Computation and Machine Learning series(book) |
Pattern Recognition and Machine Learning (Information Science and Statistics)(book) |
|---|---|---|---|---|
|
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (book) |
Artificial Intelligence: A Modern Approach (book) |
Machine Learning: A Probabilistic Perspective(book) |
Mathematics for Machine Learning (book) |
Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)(book) |
Extra: Sentdex Neural Network from Scratch, Machine Learning Mastery, Statista In-depth AI 2021.
| AI Course | Overview | Link |
|---|---|---|
| CS221: Artificial Intelligence: Principles and Techniques | A foundational AI course covering search algorithms, machine learning, and decision-making techniques. | CS221 |
| CS229: Machine Learning | One of the most popular courses at Stanford, providing a deep dive into machine learning algorithms and methods. | CS229 |
| CS231N: Convolutional Neural Networks for Visual Recognition | Focuses on deep learning and computer vision, particularly using CNNs for image recognition. | CS231N |
| CS234: Reinforcement Learning | An advanced course on reinforcement learning, covering Markov decision processes, and deep Q-learning. | CS234 |
| CS230: Deep Learning | Covers the fundamentals and advanced topics of deep learning, including neural networks and frameworks. | CS230 |
| CS224N: Natural Language Processing with Deep Learning | A course on natural language processing using deep learning, including methods like transformers and RNNs. | CS224N |
| CS229T: Topics in Machine Learning and Artificial Intelligence | A course covering advanced topics in AI and ML, including Bayesian networks and decision theory. | CS229T |
| CS231A: Computer Vision: From 3D Reconstruction to Recognition | This course focuses on computer vision and image processing, covering both theoretical and practical aspects. | CS231A |
| CS224U: Natural Language Understanding | A course focused on understanding and generating human language with AI techniques, including semantic analysis. | CS224U |
| CS109: Introduction to Probability for Computer Scientists | Covers foundational concepts in probability that are essential for AI and machine learning. | CS109 |
| CS194-26: Deep Learning for Computer Vision | Focuses on deep learning techniques used in computer vision, including image classification and object detection. | CS194-26 |
|
CS221 : Artificial Intelligence: Principle & Techniques (CLASS) |
EE 364A : Convex Optimization I (CLASS) |
EE 364B :Convex Optimization II (CLASS) |
CS 149 : Parallel Computing (CLASS) |
CS 229 : Machine Learning (CLASS-cs229m(theory)) |
|---|---|---|---|---|
|
CS 224N : Natural Language Processing with Deep Learning (CLASS) |
CS 224W : Machine Learning with Graphs (CLASS) |
CS 228 : Probabilistic Graphical Models: Principles and Techniques(CLASS) |
CS 234 : Reinforcement Learning (CLASS) |
CS 231N : Convolutional Neural Networks for Visual Recognition(CLASS) |
|
|
CS 237A : Principles of Robot Autonomy I (CLASS) |
CS 237B : Principles of Robot Autonomy II (CLASS) |
CS 238 : Decision Making under Uncertainty (CLASS) |
CS 233 : Geometric and Topological Data Analysis(CLASS) |
Notes: shervine, cs109-probability, Stanford CS 25: Transformers United V4, Stanford CS 109 - Introduction to Probability.
Good AI courses:
|
CMU : Multimodal ML (class) |
CMU : Neural Nets for NLP (class) |
CS 233 : Geometric & Topological Data Analysis (class) |
MIT 18.065 : Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (class) |
MIT : Deep Learning for Life Sciences (class) |
MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity (class) |
|---|---|---|---|---|---|
|
MIT : Machine Vision (class) |
MIT : ML for Genomics (class) |
MIT : Underactuated Robotics (class) |
UC Berkeley 287 : Advanced Robotics (class) |
MIT : Deep Learning (class) |
TinyML and Efficient Deep Learning Computing | MIT 6.S965 Fall 2022 (class) |
More : Quantum Machine Learning MOOC - Quantum ML, Parallel Computing and Scientific Machine Learning, Optimization, 18.409 Algorithmic Aspects of Machine Learning Spring 2015 MIT , Advanced NLP - CMU, Convex Optimization : CMU, MIT Generative AI Summit, MIT - EfficientML.ai Lecture, Fall 2023, MIT 6.5940, MIT 6.036 : Introduction to Machine Learning, MIT 6.S192: Deep Learning for Art, Aesthetics, and Creativity, Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy, The Most Important Algorithm in Machine Learning, Stanford Playlist : [ Stanford CS236: Deep Generative Models, Stanford EE364A Convex Optimization, Stanford CS109 Introduction to Probability for Computer Scientists, Stanford EE259: Principles of Sensing for Autonomy, Stanford CS25 - Transformers United, Stanford CS224W Machine Learning with Graphs, Stanford AA289 - Robotics and Autonomous Systems Seminar ].
| AI Conference / Competition | Overview | Link |
|---|---|---|
| NeurIPS (Conference on Neural Information Processing Systems) | One of the largest and most prestigious AI and machine learning conferences, covering deep learning, reinforcement learning, and more. | NeurIPS |
| ICML (International Conference on Machine Learning) | A leading conference in machine learning, presenting cutting-edge research in AI and ML algorithms. | ICML |
| CVPR (Conference on Computer Vision and Pattern Recognition) | A top-tier conference focused on computer vision, image recognition, and machine learning applications. | CVPR |
| AAAI (Association for the Advancement of Artificial Intelligence) | A prominent conference covering a wide range of AI topics including machine learning, robotics, and AI ethics. | AAAI |
| ICLR (International Conference on Learning Representations) | A leading conference focusing on deep learning, neural networks, and representation learning. | ICLR |
| KDD (Knowledge Discovery and Data Mining) | A major conference on data science, data mining, and AI applications, focusing on data-driven AI techniques. | KDD |
| ECCV (European Conference on Computer Vision) | One of the top conferences for computer vision research, presenting advances in machine vision and AI. | ECCV |
| ACL (Association for Computational Linguistics) | The primary conference for research in natural language processing and computational linguistics. | ACL |
| AISTATS (Artificial Intelligence and Statistics) | A conference at the intersection of AI, machine learning, and statistics, focusing on theoretical advances. | AISTATS |
| ICRA (International Conference on Robotics and Automation) | A top robotics conference, focusing on AI in robotics and autonomous systems. | ICRA |
| IJCAI (International Joint Conference on Artificial Intelligence) | A prominent international conference on AI research, covering all aspects of artificial intelligence. | IJCAI |
| Robotics: Science and Systems (RSS) | A leading conference focused on AI, robotics, and autonomous systems research. | RSS |
| MLPerf (Machine Learning Performance Benchmarking) | A global AI benchmarking competition focusing on performance comparisons for various machine learning models. | MLPerf |
| AI XPrize | A global competition challenging teams to develop AI-based solutions that positively impact humanity. | AI XPrize |
| AI & Robotics Challenge by NASA | A competition by NASA challenging teams to create AI-driven robots capable of solving space exploration tasks. | NASA AI & Robotics Challenge |
| Google AI Challenge | A competition organized by Google focusing on solving real-world problems with AI and machine learning. | Google AI Challenge |
| ImageNet Large Scale Visual Recognition Challenge | A leading competition for AI and deep learning models focused on large-scale visual recognition tasks. | ImageNet Challenge |
| Hackathons (AI Focused) | Numerous hackathons held globally, where AI enthusiasts solve real-world challenges in a short timeframe. | Devpost AI Hackathons |
| OpenAI Codex Challenge | A competition hosted by OpenAI focusing on leveraging Codex models for various coding tasks and challenges. | OpenAI Codex Challenge |
| Conference | Description |
|---|---|
| IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) | |
| International Conference on Machine Learning (ICML) | |
| Neural Information Processing Systems (abbreviated as NeurIPS) | |
| European Conference on Computer Vision (ECCV) | |
| IEEE International Conference on Robotics and Automation (ICRA) | |
| The International Conference on Learning Representations (ICLR) | |
| IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) | |
| Robotics: Science and Systems (RSS) | |
| (GPU Technology Conference) is a global AI conference. | |
| Google Cloud AI technologies | |
| ACM AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. |
[ CVPR papers ] : 2023 all day, 2022 all day, 2021 all day | NeurIPS papers, neurips2023.vizhub, neurips2023.paperdigest, rss 2022 papers, nips 2022 outstanding papers, 2021 outstanding papers.
There are also good platforms to find research publications : arXiv, google scholar : robotics, artificial intelligence, computer vision and pattern recognition, Google Quantum AI summer symposium etc.
- ICRA competitions, CVPR competitions, NeurIPS competitions, IROS competitions, NASA Space ROS Sim Summer Sprint Challenge, Autoware Challenge 2024.
- mlcontests : ml challenges and competitions list, kaggle competitions, AICrowd : runs a combination of supervised ML competitions as well as RL competitions.
- AWS deepracer : AWS DeepRacer is a beginner-friendly 3D racing simulator aimed at helping developers get started with RL.
- SC2 AI Arena : Starcraft II AI arena for RL algorithms, Gocoder.one : Coder One is an AI competition based on the classic console game, bomberman, flatland : The goal is to construct the best schedule that minimizes the delay in the requested arrival time of all trains, MineRL : MineRL is concerned with the development of sample-efficient deep RL algorithms which can solve hierarchical, sparse reward environments using human demonstrations in Minecraft, nethack, CompilerGym : CompilerGym is actually a toolkit for applying reinforcement learning to compiler optimizations, rather than a competition.
- ods.ai : ml competition platform, bitGrid : Bitgrit is a data science competition platform, numer.ai, drivendata, battlecode : The MIT 6.147 (formerly 6.370) Battlecode programming competition is a unique challenge that combines battle strategy, software engineering, and artificial intelligence. There are also lot of conference competitions happening every year.
- xeek.ai, dPhi, dataCrunch, challenge data, signate.jp, grand-challenge.
Hackathons: HackZurich, HackTUM, Stanford Treehacks, HackMIT, NASA Space app challenge, pytorch summer hack, esa socis etc.
Summer Schools are focused training camps for different AI methods and this list has very popular and good AI summer schools. Some additional summer schools (mostly European):
- Princeton Machine Learning Theory Summer School, LogML : London Geometry and ML, Nordic Probabilistic AI school, Summer schools in Europe
- IDESSAI 2022 - SECOND JOINT SUMMER SCHOOL OF INRIA AND DFKI, The European AI Summit 2022, NORA.ai Summer School
additionally, Google events, IEEE & ACM Summer Schools, AI Residency programs: this list + Microsoft, Meta AI, Nvidia, Apple, OpenAI, MIT IDSS etc
AI Training: Several premier professional AI training programs and certifications:
- Stanford SCPD Graduate AI Certificate, Robotics and Autonomous Systems certificate.
- MIT Professional Certificate Program in Machine Learning & Artificial Intelligence; Designing and building AI products and services.
- Udacity Nanodegree: > School of Autonomous Systems and > School of Artificial Intelligence, Harvard Extension School : data science graduate certificate.
- NVIDIA training institute : Some example courses are Building Real-Time Video AI Applications, Building Video AI Applications at the Edge on Jetson Nano etc
- AI resources from Google : Google Cloud ML & AI Engineer path, Grow with Google, Google AI Experiments, Google AI Education, Google Quantum AI, Google Developer Experts : AI
- TensorFlow Learn ML resources & TensorFlow courses, Deepmind Learning resources, Using Python for Research (beginner).
There exists very good AI blogs such as: AppliedAI: AI in mobility (***), Google AI blogs, machinelearning mastery, c-olah blog, BAIR, MIT Technology Review, Guardian's AI section. Here is an updated list of all AI seminars and this medium article summarizes many resources. Also some important youtube AI channels with good content - StatQuest, Yannic Kilcher, 2 minutes paper, 3blue1brown, stanfordonline, sentdex etc.
Professional Certifications:
- EITCA - AI : European Artificial Intelligence Certificate (***), Google Cloud Professional ML Engineer, Microsoft Azure AI Engineer Associate, tensorflow developer certificate
AI Product Manager: AI Product manager specialization, AI product manager nanodegree.
Fellowships: Government of AI Fellowship, Foresight Fellowship, Pi School, OpenLab.
| AI Research Lab / Group | Overview | Link |
|---|---|---|
| OpenAI | An AI research lab dedicated to ensuring that artificial general intelligence (AGI) benefits humanity. | OpenAI |
| DeepMind | A subsidiary of Alphabet focused on solving intelligence and advancing science through AI research. | DeepMind |
| Google Brain | A team within Google focusing on deep learning and AI research, often developing breakthrough technologies. | Google Brain |
| Facebook AI Research (FAIR) | Facebook's AI research group working on fundamental AI research and advancing machine learning technologies. | FAIR |
| Microsoft Research AI | A division within Microsoft Research dedicated to advancing AI through interdisciplinary research. | Microsoft Research AI |
| Stanford AI Lab (SAIL) | A leading AI research group within Stanford University, working on various AI subfields and applications. | Stanford AI Lab |
| MIT CSAIL | The Computer Science and Artificial Intelligence Laboratory at MIT, one of the leading centers for AI research. | MIT CSAIL |
| Carnegie Mellon University (CMU) AI | A pioneering AI research group within CMU, known for work in robotics, machine learning, and AI theory. | CMU AI |
| Berkeley AI Research (BAIR) | A research group at UC Berkeley that explores a broad range of AI topics, including reinforcement learning, robotics, and vision. | BAIR |
| University of Toronto AI | The AI research group at the University of Toronto, famous for work in deep learning and neural networks. | UofT AI |
| University of Oxford AI | Oxford's AI research focuses on machine learning, robotics, and AI ethics. | Oxford AI |
| Huawei Cloud AI | AI research and applications within Huawei's cloud computing platform, advancing NLP, computer vision, and more. | Huawei Cloud AI |
| Amazon AI | Amazon's research division focused on machine learning, deep learning, and AI innovations. | Amazon AI |
| Tencent AI Lab | Tencent's AI research group exploring AI technologies for social media, gaming, and finance. | Tencent AI Lab |
| NVIDIA AI Research | NVIDIA's AI research group, focused on accelerating AI and deep learning through hardware and software advancements. | NVIDIA AI Research |
| Baidu AI Research | Baidu's research division advancing AI across search, NLP, and autonomous driving. | Baidu AI |
| AI2 (Allen Institute for AI) | A non-profit AI research organization focused on AI research in NLP, computer vision, and machine reasoning. | AI2 |
| The Institute for AI and Automation (IAIA) | A research group focused on AI applications in automation, robotics, and autonomous systems. | IAIA |
| ETH Zurich AI | AI research within ETH Zurich, one of Europe's leading technical universities, focusing on machine learning, robotics, and optimization. | ETH Zurich AI |
| AI Lab at University of Cambridge | A leading AI research group within the University of Cambridge focusing on machine learning, vision, and ethics. | Cambridge AI Lab |
| Research at IBM Watson | IBM’s AI research group, particularly known for its work in natural language processing and healthcare. | IBM Watson Research |
| AI Research at Adobe | Adobe’s AI research group focused on creative AI tools, vision, and natural language processing. | Adobe AI Research |
| Apple AI Research | Apple’s AI division working on AI-driven innovation for its ecosystem, including Siri, vision, and health applications. | Apple AI Research |
| Facebook Reality Labs AI | Facebook’s research division that combines AI with AR and VR technologies. | Facebook Reality Labs |
| Turing Institute | The UK’s National Institute for Data Science and AI, focused on research in AI ethics, machine learning, and statistics. | Turing Institute |
| AI Lab at UC San Diego | The AI lab at UCSD working on AI in the domains of machine learning, robotics, and computer vision. | UCSD AI Lab |
| Max Planck Institute for Intelligent Systems | A leading institute for AI research in Europe, focusing on machine learning, robotics, and cognitive systems. | Max Planck Institute for Intelligent Systems |
These are premier AI research labs who give out cutting edge AI possibilities:
Stanford AI, MIT CSAIL , Berkeley AI Research (BAIR), CMU AI, CMU Robotics. Laboratory for Vision and Artificial Intelligence (LIVIA), UPenn GRASP, EPFL - CIS, MLO Lab, MIT SparkLab. JP Morgen AI Research Lab, Elkanio Research Labs, Tesla AI. Microsoft AI Research, Meta AI (FAIR), Deepmind, OpenAI Many premier AI research labs provide courses via coursera, edX, udemy etc.
Europe : ETH-Z, inria, IDSIA, Norwegian AI research consortium (NORA), Google AI Zurich, Apple AI Zurich, ETH-Z AI, ETH-Z Robotic System Lab, ETH-Z drone projects. {NaverLabs Europe](https://europe.naverlabs.com/), Vision4AI.eu, ellis.eu (European Laboratory for Learning and Intelligent Systems), Institute of ethical AI and ML, Alan Turing Institute, sustainable-ai.eu, HPE HPC/AI EMEA RESEARCH LAB (ERL), EUROPEAN AI LANDSCAPE, Zurich NLP group, IDIAP. CLAIRE AI Network : All the best EU AI research labs are listed here in this document! (***) ALL EU AI startups: check this! it lists all AI startups in EU solving problems from different industries. We can also check AI use cases in different industries via AMAI AI experts.
AI Research Labs & Groups in Germany:
German Research Center for Artificial Intelligence ( Deutsches Forschungszentrum für Künstliche Intelligenz ) : DFKI is the federal government body for KI : This DFKI - Robotics Innovation Center is nearby in Bremen. x Several other important AI centres : TÜBINGEN AI CENTER , KI-Berlin, KI.NRW, appliedAI.de, Network of National Centres of Excellence for AI Research. This is such a beautiful document to understand ai research in germany. also cyber-valley : is Europe's largest research consortium in the field of artificial intelligence The state of Baden-Württemberg, the Max Planck Society with the Max Planck Institute for Intelligent Systems, the Universities of Stuttgart and Tübingen as well as Amazon, BMW AG, IAV GmbH, Mercedes-Benz Group AG, Dr. Ing. hc F. Porsche AG, Robert Bosch GmbH and ZF Friedrichshafen AG are the founding partners of this initiative. In addition, the Fraunhofer-Gesellschaft is a Cyber Valley partner. Cyber Valley is also supported by the Christian Bürkert Foundation, the Gips-Schüle Foundation, the Vector Foundation and the Carl Zeiss Foundation.
TUM Vision groups, AI4EO (Artificial Intelligence for Earth Observation), Bosch AI, IBM research, Uni-Freiburg, LeibnizAI Lab, DLR Institute for Robotics and Mechatronics, TUD AIML group. Saarbrücken and Tübingen seems to have lot of AI institutes.
AI Research Labs & Groups in Hamburg:
ARtificial Intelligence Center Hamburg (ARIC), AI.hamburg, TUHH AI research publications, Universität Hamburg AI & Robotics group.
| Dataset Source | Overview | Link |
|---|---|---|
| Kaggle Datasets | A platform with thousands of datasets for various ML tasks such as image processing, text, etc. | Kaggle Datasets |
| UCI Machine Learning Repository | One of the oldest sources for datasets used in classification, regression, clustering, and more. | UCI Repository |
| Google Dataset Search | A search engine to find datasets across the web from various domains. | Google Dataset Search |
| AWS Public Datasets | A collection of public datasets for fields like genomics, astronomy, and machine learning. | AWS Public Datasets |
| OpenML | A platform to share datasets, experiments, and ML models, enabling collaboration. | OpenML |
| Microsoft Research Open Datasets | Datasets across a variety of fields such as computer vision, NLP, and more. | Microsoft Research Datasets |
| Data.gov | US government datasets across domains like health, education, economics, and climate. | Data.gov |
| TensorFlow Datasets | A collection of ready-to-use datasets for TensorFlow, covering CV, NLP, and other ML tasks. | TensorFlow Datasets |
| Fast.ai Datasets | Curated datasets for deep learning and computer vision tasks. | Fast.ai Datasets |
| Census Data (U.S.) | Datasets with demographic, economic, and social information from the US Census Bureau. | U.S. Census Data |
| ImageNet | A large-scale dataset for image classification and object detection tasks. | ImageNet |
| COCO Dataset | Large-scale dataset for object detection, segmentation, and image captioning. | COCO Dataset |
| TMDb (The Movie Database) | Provides datasets related to movies and TV series metadata, reviews, and user ratings. | TMDb API |
| Stanford Large Scale Video Dataset | Dataset for computer vision tasks like action recognition, video summarization, etc. | Stanford Video Dataset |
| Yelp Dataset | Contains business reviews, ratings, and user information, often used for sentiment analysis. | Yelp Dataset |
| Papers with Code | Platform to find datasets alongside machine learning papers and implementations. | Papers with Code |
| The European Data Portal | Open datasets from European Union governments, covering a wide range of domains. | European Data Portal |
| Zenodo | A general-purpose open-access repository for scientific datasets. | Zenodo |
| KDD Cup Datasets | Datasets used in KDD Cup competitions for tasks such as classification and clustering. | KDD Cup Datasets |
| Government Open Data Platforms (Global) | Open data from various countries including UK, EU, and Canada. | UK Data Service, Open Data Canada, European Data Portal |
PUBLIC datasets:
- Google dataset search | kaggle dataset | paperswithcode dataset | openml-dataset | nasa earth dataset (largest collection of geo-related datasets about the earth, climate and water bodies.) | AWS opendata | Azure opendata | data.world | huggingface | UCI ML dataset | datahub.io
- github/awesome-public-dataset | govdata.de | destatis.de | data.gov | visualdata.io (Computer Vision datasets) | CMU library dataset , TUHH x COMPUTER VISION datasets: xView, ImageNet, google open images, IMDB-wiki (annotated face images), dog-breed dataset, TUM
- kinetic 700-2020 (human poses from YT videos), cityscape (semantic segmentation)
- colors with RGB values. x NLP datasets: quantumstat, QA, amazon reviews, rotten tomato reviews x Sentiment analysis: IMDB reviews, stanford sentiment, twitter US airlines x Self Driving car dataset: waymo, berkeley deepdrive, WPI dataset (traffic lights, pedestrian, and lane detection), bosch small traffic light, comma.ai (car’s speed, acceleration, steering angle, and GPS coordinates), MIT driveseg, UCSD-LISA. x geo & satellite datasets::
- URBAN DATA PLATFORM : MetaVER, geoportal, transparenzportal, dlr eoc, GIS data, UCSC, geodaten
- Satellite data: Copernicus, DLR PRIVATE datasets | planet, arcGIS etc.
| Service | Overview | Link |
|---|---|---|
| Papers with Code | A platform that connects machine learning papers, datasets, and code implementations for ML tasks. | Papers with Code |
| Weights & Biases | A platform for tracking machine learning experiments, visualizing metrics, and collaborating on models. | Weights & Biases |
| AllThingsAI | An AI resource hub offering research, articles, datasets, and tools for the AI community. | AllThingsAI |
| Google AI Hub | A platform for sharing machine learning code, models, and datasets built with TensorFlow and TFX. | Google AI Hub |
| MLflow | Open-source platform for managing the ML lifecycle, including experimentation, reproducibility, and deployment. | MLflow |
| Comet | A tool for tracking experiments, visualizing metrics, and collaborating with teams in machine learning projects. | Comet |
| Neptune.ai | A platform for experiment tracking, model monitoring, and collaboration on AI projects. | Neptune.ai |
| Hugging Face | A platform and library for Natural Language Processing (NLP) models, datasets, and pre-trained models. | Hugging Face |
| TensorBoard | TensorFlow's visualization tool for model training, metrics, and graphs. | TensorBoard |
| Amazon SageMaker | A managed service for building, training, and deploying machine learning models at scale. | Amazon SageMaker |
| Azure Machine Learning Studio | A cloud-based machine learning development environment for building, training, and deploying models. | Azure Machine Learning |
| Google Cloud AI Platform | A managed service for training, deploying, and managing ML models on Google Cloud. | Google Cloud AI |
| IBM Watson Studio | A platform for building and deploying AI and machine learning models with integrated tools. | IBM Watson Studio |
| Run.ai | A platform that optimizes and manages AI infrastructure, improving efficiency and collaboration in ML teams. | Run.ai |
| DataRobot | An enterprise AI platform for automating machine learning, including model training and deployment. | DataRobot |
| Fritz AI | A platform focused on building, training, and deploying AI models for mobile and edge devices. | Fritz AI |
| Optuna | An open-source hyperparameter optimization framework designed to automate the process of model tuning. | Optuna |
| Peltarion | A platform for building and deploying AI models, with a focus on production and scaling AI applications. | Peltarion |
| ClearML | A platform for experiment management, dataset versioning, and model deployment. | ClearML |
| KubeFlow | A Kubernetes-native platform for managing end-to-end ML workflows, from data preparation to deployment. | KubeFlow |
| Kaggle Kernels | A platform that allows data scientists to write, run, and share Jupyter notebooks in an interactive environment. | Kaggle Kernels |
| Apache Mahout | A machine learning library built on top of Hadoop, designed for scalable machine learning algorithms. | Apache Mahout |
| BigML | A platform offering easy-to-use machine learning services for businesses to integrate ML models. | BigML |
| RapidMiner | A data science platform that provides tools for machine learning, data mining, and predictive analytics. | RapidMiner |
| Dataiku | A collaborative data science platform for building, deploying, and managing AI and ML applications. | Dataiku |
| ML Kit | A set of machine learning APIs from Google, designed for on-device processing for mobile applications. | ML Kit |
| Alteryx | A platform for data blending, advanced analytics, and machine learning, helping teams work with data at scale. | Alteryx |
| Tibco Spotfire | A data analytics and machine learning platform, focused on providing real-time data insights and visualizations. | Tibco Spotfire |
| Datarobot AI Cloud | A comprehensive enterprise AI platform that accelerates data science projects with automation. | Datarobot AI Cloud |
| Zeroth | A platform for building and scaling AI-powered applications with a focus on data-centric AI workflows. | Zeroth |
| MLflow | Open-source platform to manage the ML lifecycle, including experimentation, reproducibility, and deployment. | MLflow |
A curated directory of the latest AI tools & services. Discover the best tools, services and resources at the forefront of AI.
This is a very amazing platform where you get access to categorized state of the art research along with open source code (if available / published by authors in github). Based on the problem statement and requirement, we can filter the best research work done for the problem and get the open source code for applied ml.
+ paperswithcode/[vision](https://paperswithcode.com/area/computer-vision)
+ paperswithcode/[nlp](https://paperswithcode.com/area/natural-language-processing)
The developer-first MLOps platform Build better models faster with experiment tracking, dataset versioning, and model management
Turn websites into LLM-ready data. Power your AI apps with clean data crawled from any website.
Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.
Build and scale trusted AI on any cloud. Automate the AI lifecycle for ModelOps.
Log, organize, compare, register, and share all your ML model metadata in a single place. Automate and standardize as your modeling team grows
Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform. Google provides a lot of ML services like AutoML, diagflow, deep learning containers etc, here is the list.
Generative AI from Google.
Build, train and deploy state of the art models powered by the reference open source in machine learning.
Research : [ An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows, Active Vision Reinforcement Learning under Limited Visual Observability, Mixture-of-Experts (MoE) - Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding, Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity; GraphCast: AI model for faster and more accurate global weather forecasting, Swashplateless-elevon Actuation for a Dual-rotor Tail-sitter VTOL UAV, DynIBaR: Neural Dynamic Image-Based Rendering, CrossLoco: Human Motion Driven Control of Legged Robots via Guided Unsupervised Reinforcement Learning, Pearl: A Production-ready Reinforcement Learning Agent, Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data, Learning to Fly in Seconds, Graph neural networks in TensorFlow, BlackMamba: Mixture of Experts for State-Space Models, YOLO-World: Real-Time Open-Vocabulary Object Detection, Cached Transformers: Improving Transformers with Differentiable Memory Cache, Exphormer: Sparse Transformers for Graphs, Solving olympiad geometry without human demonstrations, Lumiere: A Space-Time Diffusion Model for Video Generation, Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads, Mamba: Linear-Time Sequence Modeling with Selective State Spaces ]
News & Resources: Hands-on with Gemini: Interacting with multimodal AI, Gemini: Excelling at competitive programming, Gemini: Unlocking insights in scientific literature, Andrej Karpathy - Neural Networks: Zero to Hero, MIT 8.962 General Relativity, Spring 2020, MIT 18.100A Real Analysis, Fall 2020, MIT 6.006 Introduction to Algorithms, Spring 2020, Why Neural Networks can learn (almost) anything, Watching Neural Networks Learn, Deriving the Transformer Neural Network from Scratch #SoME3, MIT 6.S191: Recurrent Neural Networks, Transformers, and Attention, Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Paper Explained), Efficient Streaming Language Models with Attention Sinks (Paper Explained), Mamba - a replacement for Transformers?, Reinforced Self-Training (ReST) for Language Modeling (Paper Explained), The math behind Attention: Keys, Queries, and Values matrices, Stanford Seminar - Robot Learning in the Era of Large Pretrained Models, Stanford Seminar - Robot Skill Acquisition: Policy Representation and Data Generation.