Here is a great set of cheat sheet on some of the following topics:
- Supervised learning
- Unsupervised learning
- Deep learning
- Probability and statistics
- Linear algebra
- Tips and tricks including performance metrics
https://stanford.edu/~shervine/teaching/cs-229/
Hope you liked the cheat sheets on different topics of machine learning and data science.
I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
Latest posts by Ajitesh Kumar (see all)
- Retrieval Augmented Generation (RAG) & LLM: Examples - February 15, 2025
- How to Setup MEAN App with LangChain.js - February 9, 2025
- Build AI Chatbots for SAAS Using LLMs, RAG, Multi-Agent Frameworks - February 8, 2025
I found it very helpful. However the differences are not too understandable for me