Lecture 01 - The Learning Problem

Caltech machine learning intro course: ▶ this video karpathy's Zero to Hero series ( link ) meta llama 2 - is open source link Tools: ai - hosting Banana - Machine Learning Model Deployment on Serverless GPUs link pinecone - vector database: link how to run AI language models on a single cpu pc - link

I have found the Caltech machine learning intro course to be the most elucidating of any resource so far: ▶ this video The delivery of the lecture material was like butter to me. Might work differently for others though.

Pattern Recognition and Machine Learning by Christopher Bishop. Book: link Notes: Its very very math heavy but if you really want to grasp the concepts and the idea around each topic, this is one of the way to go. Online Lectures: ▶ this video I like how he explains stuff and adds some context behind the math of each topic. Alternative: link A pretty good youtube channel that follows up on modern machine learning and he has all of his video tutorial demos on GitHub. Hope this helps fellow Machine Learner :)

I'm a neophyte to ML, but one course I very much enjoyed auditing was "Learning from Data" [1] taught by Caltech Professor Yaser Abu-Mostafa. I found concept of the _Learning Model_ particularly fresh representation of the ML activity (starting at 14:39 in the first video linked here [2]) [1]: link [2]: ▶ this video

there are some lectures about machine learning on youtube. I think they are good to watch: ▶ this video and link and my favorite channels are Standford and MIT open courseware link and link

link ▶ this video (incidentally for graycat, Yaser Abumostafa is a Muslim Egyptian immigrant from Cairo) He covers the VC dimension in link , and leaves the proof to an appendix of the book.