Don't miss any moment of global AI innovation
Daily three-minute AI industry trends
AI industry milestones
Lists all AI hardware products.
AI monetization case sharing
AI image creation monetization cases
AI video creation monetization cases
AI audio creation monetization cases
AI content writing monetization cases
Free sharing of the latest AI tutorials
Shows total visits ranking of AI websites
Track fastest growing AI websites by traffic
Focus on AI websites with significant traffic drops
Shows weekly visits ranking of AI websites
AI websites most popular with US users
AI websites most popular with Chinese users
AI websites most popular with Indian users
AI websites most popular with Brazilian users
Total visits ranking of AI image generation websites
Total visits ranking of AI personal assistant websites
Total visits ranking of AI character generation websites
Total visits ranking of AI video generation websites
GitHub popular AI projects by total stars
GitHub popular AI projects by growth rate
GitHub popular AI developer ranking
GitHub popular AI organization ranking
GitHub popular deepseek open source projects
GitHub popular TTS open source projects
GitHub popular LLM open source projects
GitHub popular ChatGPT open source projects
Overview of GitHub popular AI open source projects
Exercise solutions to "R for Data Science"
Deep Learning for humans
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Streamlit — A faster way to build and share data apps.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Build and share delightful machine learning apps, all in Python. ? Star to support our work!
Roadmap to becoming an Artificial Intelligence Expert in 2022
Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.
10 Weeks, 20 Lessons, Data Science for All!
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
