PickVidsBest Machine learning & deep learning Videos
「Machine learning & deep learning」下面有131部被不同的人、在不同月份推荐过的视频。挑的时候先看对象：入门25部，适合还没学过的人；进阶51部，适合已经懂术语的人；普通向38部，按兴趣就能看；更硬的17部，留给想看推导或细节的人。这一类数量最多的是演讲，所以它们的共同点是一位讲者把一个判断讲完，例子是为了撑住这个判断。常出现的频道包括Andrej Karpathy、3Blue1Brown、Google TechTalks。目录给这个主题划的范围是：Machine learning: neural networks, deep learning, backpropagation, training, optimization, reinforcement learning, AlphaGo, ML research lectures, Karpathy, Hinton, LeCun, Stanford CS231n.如果只看三部，就从《The spelled-out intro to neural networks and backpropagation: building micrograd》开始，它负责把问题讲出画面；然后看《But what is a neural network? | Deep learning chapter 1》，换一种讲法或例子；最后看《AlphaGo - The Movie | Full award-winning documentary》，留给已经有基础的人。页面按每页24部切开，顺序看的是被推荐跨越了多少个月，不是播放量。每条标题下面那一句是这部片子自己的内容说明，用来区分它在讲定义、过程、演示还是观点。点进去能看到推荐原话、原文链接和适合谁看。看的时候把强烈推荐当成有人明确叫你去看，把引用当成只是拿来举例，把批评留下来对照，不要把每条评论都当成同一力度。
Hello World - Machine Learning Recipes #1
The first episode of Google's Machine Learning Recipes series.
Attention in transformers, step-by-step | Deep Learning Chapter 6
3Blue1Brown's visual walkthrough of the attention mechanism inside transformers, covering self-attention, multiple heads and cross-attention.
GDC 2018: John McDonald (Valve) - Using Deep Learning to Combat Cheating in CSGO
A GDC 2018 talk by Valve's John McDonald on VACNet, a deep learning system that analyzes CS:GO gameplay to detect cheaters.
LSTM is dead. Long Live Transformers!
Leo Dirac explains how transformer models have replaced LSTMs in NLP.
George Hotz: Hacking the Simulation & Learning to Drive with Neural Nets | Lex Fridman Podcast #132
Lex Fridman interviews George Hotz, founder of Comma.ai, in a three-hour conversation.
Attention Is All You Need
Yannic Kilcher walks through the Attention Is All You Need paper, which introduced the Transformer, a sequence model based solely on attention without recurrence or convolutions.
The Simple Essence of Automatic Differentiation - Conal Elliott
Conal Elliott presents his paper on automatic differentiation, deriving a simple, generalized AD algorithm from a natural specification.
Backpropagation, intuitively | Deep Learning Chapter 3
3Blue1Brown's third deep learning chapter gives a visual, intuitive account of backpropagation and what happens to a neural network as it learns.
Training AI Without Writing A Reward Function, with Reward Modelling
Robert Miles explains reward modelling, in which an AI learns what humans want from their preference comparisons instead of a hand-written reward function.
Ali Rahimi's talk at NIPS(NIPS 2017 Test-of-time award presentation)
Ali Rahimi's NIPS 2017 Test-of-Time award talk, in which he argues that modern machine learning has become alchemy, working without being rigorously understood.
Playing a Neural Network's version of GTA V: GAN Theft Auto
Sentdex demonstrates GAN Theft Auto, a GameGAN-based neural network that recreates GTA V so the network itself is the playable environment.
Connections between physics and deep learning
Max Tegmark's MIT CBMM talk on why deep learning works so well.
Deepmind artificial intelligence @ FDOT14
A short smartphone-recorded clip from a 2014 event in Paris where DeepMind CEO Demis Hassabis presents an AI learning to play old Atari games.
Gradient descent, how neural networks learn | Deep Learning Chapter 2
3Blue1Brown's second neural network chapter explains cost functions and gradient descent, showing how a network learns by minimizing error during training.
Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton
Geoffrey Hinton's 2025 Royal Institution Discourse on what AI has learned from biological intelligence and how it might surpass it.
ai.bythebay.io: George Hotz, Self-Driving Lessons from Comma AI
George Hotz of Comma AI gives a 2017 conference talk on lessons from building a self-driving system.
Lecture 1 | Natural Language Processing with Deep Learning
The first lecture of Stanford's Natural Language Processing with Deep Learning course, taught by Chris Manning and Richard Socher.
Stealing Baseball Signs with a Phone (Machine Learning)
Mark Rober and Jabril build an app that uses machine learning to decode baseball sign sequences.
State of GPT | BRK216HFS
Andrej Karpathy walks through how GPT assistants are trained, from tokenization and pretraining to supervised finetuning and RLHF, and gives practical prompting strategies and a view of the tooling ecosystem.
Neural Networks Demystified [Part 1: Data and Architecture]
The first of a short series by Welch Labs on building a complete neural network in Python.
Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI | Lex Fridman Podcast #333
Lex Fridman's long conversation with Andrej Karpathy on Tesla's self-driving approach, Optimus, neural networks, transformers, AGI and more.
Do we need Attention? - Linear RNNs and State Space Models (SSMs) for NLP
Sasha Rush surveys linear RNNs and state space models as replacements for attention in transformer-style models, in a talk for MLSys.
[CVPR'21 WAD] Keynote - Andrej Karpathy, Tesla
Andrej Karpathy, then Tesla's head of AI, gives a CVPR 2021 workshop keynote on how Autopilot's neural networks work.
Google Developers Day US - Theorizing from Data
Peter Norvig's 2007 Google talk on theorizing from data, covering what huge datasets can do for language understanding, translation, information extraction and inference.