• ML 101
• Zero to Hero
• openwebtext2
• https://pile.eleuther.ai
• GPT-2 Output Detector https://openai-openai-detector.hf.space
• AI Content Detector - writer.com https://writer.com/ai-content-detector
• AI DETECTOR - content at scale https://contentatscale.ai/ai-content-detector
• GPTZero https://gptzero.me
• VisualGPT https://github.com/microsoft/visual-chatgpt
• Tokenizer https://platform.openai.com/tokenizer
• SentenceTransformers Sentence-BERT
• Promptingguide.ai
• Playground https://platform.openai.com/playground
• DALL-E https://labs.openai.com
• Codex JavaScript Sandbox https://platform.openai.com/codex-javascript-sandbox
• OpenAI Cookbook https://github.com/openai/openai-cookbook
• VALL-E https://vall-e.io
• https://lablab.ai
• https://azureaidevs.github.io/hub
• https://microsoft.github.io/HAXPlaybook
• Transformers
• https://udlbook.github.io/udlbook/
• https://vicuna.lmsys.org
• Machine Learning by Tom Mitchell
• Interpretable ML book - Explainable AI
• https://machinelearningmastery.com
• https://github.com/alicezheng/feature-engineering-book
• https://towardsdatascience.com/the-7-steps-of-machine-learning-2877d7e5548e
• https://selfdrivingcars.mit.edu
• https://hadrienj.github.io/posts/Deep-Learning-Book-Series-2.1-Scalars-Vectors-Matrices-and-Tensors
• https://becominghuman.ai/building-an-image-classifier-using-deep-learning-in-python-totally-from-a-beginners-perspective-be8dbaf22dd8
• http://csail.mit.edu
• https://ai.google
• https://aischool.microsoft.com
• https://github.com/amitpuri/data-science-blogs
• https://www.scrapingdog.com/blog/web-scraping-r
• https://khuyentran1401.github.io
• https://mathdatasimplified.com
• https://superstudy.guide
• LearnAI Boot Camp Training Materials
• Anomaly Detector API
• ONNX Runtime
• NNI (Neural Network Intelligence)
• ETHICAL AI
• AutoML
  • https://ml.azure.com
  • https://aka.ms/mythbuster-automl
  • https://aka.ms/automl-metrics
• Shervine Amidi's illustrated study guides for Data Science tools
• Practical Deep Learning for Coders part 2: Deep Learning Foundations to Stable Diffusion
• 6.S191: Introduction to Deep Learning
• Neural Networks: Zero to Hero
• Machine Learning Engineering for Production
• 11-777: Multimodal Machine Learning
• Practical Deep Learning for Coders
• CS25: Transformers United
• NYU-DLSP21: NYU Deep Learning Spring
• NLP Course
• Dive into Deep Learning
• Reinforcement Learning Course
• CS224N: Natural Language Processing with Deep Learning
• Deep Learning Lecture Series
• Stanford CS221: Artificial Intelligence: Principles and Techniques
• CS231n: Deep Learning for Computer Vision
• https://github.com/ashishpatel26/Treasure-of-Transformers
• https://rl-at-scale.github.io
• https://machinelearning.apple.com/research/neural-3d-relightable
• https://github.com/facebookresearch/dinov2
• https://www.futuretools.io