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Why We Use p<0.05 🔹 Ever wondered why scientists often use p<0.05 as the threshold for statistical significance? Let's dive into its history, implications, and alternative approaches. 🔹 The p<0.05 threshold can be traced back to Sir Ronald A. Fisher in the 1920s. He suggested…
Bayes' Theorem Unpacked 🕵️♂️📊 🧵 Let's delve into the brilliant world of Bayes' theorem, a cornerstone of probability and modern machine learning. Hold tight, we're making it simple! • Starting with a Question: Ever changed your mind after getting new information? That's…
Want to learn about meta-learning & few-shot learning? All of the latest lecture videos for Stanford CS330 are now online! youtube.com/playlist?list=… New topics in Fall '22 include: - self-supervised pre-training - large scale meta-optimization - domain adaptation & generalization
Exciting first day co-teaching @drmapavone’s AA203: Optimal and Learning-Based Control, with @spenMrich at @StanfordEng! Interested in the intersections between optimal control and RL? Look no further, all course materials will be available at: stanfordasl.github.io/aa203/
As PhD visit days are coming up, I'd like to share this collated resource for prospective & current PhDs, covering how to choose advisors & schools, advice for research, teaching, fellowships, networking & more github.com/pliang279/awes… Credit to the original authors of each link!
This function has saved me many hours of debugging tensor shapes and dtypes in python code. It's a battle-hardened one-liner to print a table of names and properties. gist.github.com/nmwsharp/54d04… Works for #numpy, #pytorch, #jax and probably others. Try it out!
The first NeRF in a TV commercial! It starts with a scene of a younger version of me, and then transitions to present-day me Directed by @nortondirector using @LumaLabsAI for @McDonalds *at least, it's the first commercial that I know using NeRF, if you know of another lmk :)
Single brain cell looking for a connection. source: old.reddit.com/r/interestinga…
AI Research Experience - Harvard CS197 AI Research course and book that teaches how to do cutting-edge research, research workflows, and using tools commonly used in AI research(like PyTorch, Lightning, Hugging Face, and more). Course book: cs197.seas.harvard.edu
🔥 New (1h56m) video lecture: "Let's build GPT: from scratch, in code, spelled out." youtube.com/watch?v=kCc8Fm… We build and train a Transformer following the "Attention Is All You Need" paper in the language modeling setting and end up with the core of nanoGPT.
I am delighted to announce that the "real" camera-ready version of my new book, "Probabilistic Machine Learning: Advanced Topics", is now available. It will appear in print this summer, but it is already freely available online at probml.github.io/book2.
Multi-object tracking methods often feel hacky. It can be frustrating to read a nice paper, only to find a series of "post-processing" hacks to improve the numbers. It is as if the tracking problem could not be solved in a single step... Are you feeling this too? Let's dig👇
A small update on the history of SLAM, and its future :) Last year, NeRFs sparked a new and ongoing era of geo/photometric SLAM! What lies ahead? I think some exciting avenues will leverage LLMs, diffusion models, or use light fields as maps! Same problem, new tools🚀
Excited to share that my day-long workshop (a short course) on #ExplainableAI is now publicly available as a five-part youtube video lecture series. Link to video lectures: lnkd.in/gzfmJug9 Link to slides: lnkd.in/e_RsBVPx #AI #ML @trustworthy_ml @XAI_Research
#GraphNeuralNetworks are way too cool to be left unexplored! In a nutshell, GNNs are an exciting merger between graph theory (math) & #DeepLearning (coding). Here's my detailed resource stack of best GNN theory explainers, videos & coding tutorials I used for my own learning.
Grad school applicants: my Computational Robotics group at Harvard SEAS is hiring PhD students in Fall 2023! If you are interested in robotics, computer vision, machine learning, applied math, and their application in safe autonomy, you are the person I am looking for!
arXiv is a free distribution service and an open-access archive for 2,147,623 scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, etc. I’ve read all those articles so you don’t have to. Here is what I’ve learned. A 🧵 👇 1/2,147,624
A visual system with shape perception in plants?!? Source: vm.tiktok.com/ZMFrd7SHp/
Our team at Apple has PhD internship positions in the area of 3D computer vision and neural rendering for a duration of 6+ months. Please DM if you are interested.
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