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。
The spelled-out intro to neural networks and backpropagation: building micrograd
Andrej Karpathy's first Neural Networks: Zero to Hero lecture, which builds micrograd, a tiny autograd engine, from scratch in Python.
But what is a neural network? | Deep learning chapter 1
3Blue1Brown's first chapter on deep learning explains what neurons and layers are and the math underlying a basic neural network, using handwritten digit recognition with visual animations.
AlphaGo - The Movie | Full award-winning documentary
Feature-length documentary following DeepMind's AlphaGo program as it prepares for and plays the 2016 match against Go champion Lee Sedol.
Transformers, the tech behind LLMs | Deep Learning Chapter 5
3Blue1Brown's visual explanation of how transformers and GPT-style language models work, tracing data through tokens, embeddings and attention.
USENIX Security '18-Q: Why Do Keynote Speakers Keep Suggesting That Improving Security Is Possible?
James Mickens's humorous USENIX Security keynote argues that improving security and trusting opaque technology like machine learning is far harder than the industry admits.
Lecture 1 | Machine Learning (Stanford)
The first lecture of Andrew Ng's Stanford CS229 Machine Learning course, giving an overview of the course.
MarI/O - Machine Learning for Video Games
SethBling shows MarI/O, a program using neural networks and the NEAT genetic algorithm to learn to play Super Mario World.
Deep Dive into LLMs like ChatGPT
Andrej Karpathy's three-hour-plus general-audience deep dive into how ChatGPT-style LLMs are built, covering the full training stack from pretraining to post-training.
Computer program that learns to play classic NES games
Tom7 explains and demos software that learns to play NES games by finding score patterns in memory and then searching for inputs that raise it.
RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning
The first lecture of David Silver's reinforcement learning course, introducing the field and its basic concepts.
Let's build GPT: from scratch, in code, spelled out.
Andrej Karpathy builds a GPT from scratch in code, following Attention Is All You Need and GPT-2/3, in about two hours using PyTorch.
Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning
Andrew Ng presents unsupervised feature learning and deep learning, showing how algorithms can learn representations from unlabeled data instead of hand-designed features.
A Hackers' Guide to Language Models
Jeremy Howard's 90-minute practical tour of language models.
Andrej Karpathy - AI for Full-Self Driving at Tesla
Andrej Karpathy, then Tesla's head of AI, presents at ScaledML 2020 on how Tesla builds full self-driving using vision-based neural nets and fleet data collection.
[1hr Talk] Intro to Large Language Models
Andrej Karpathy gives a one-hour general-audience introduction to large language models, covering what they are, how they are trained, comparisons to operating systems, and security challenges such as jailbreaks.
Joscha Bach: Artificial Consciousness and the Nature of Reality | Lex Fridman Podcast #101
Lex Fridman has a three-hour conversation with Joscha Bach about artificial consciousness, the workings of the mind, intelligence and the possibly simulated nature of reality.
Tensorflow and deep learning - without a PhD by Martin Görner
Martin Gorner's Devoxx talk introduces deep learning with TensorFlow, walking through neural networks for MNIST, how to choose an architecture and how to diagnose networks that fail to train.
Brains, Sex, and Machine Learning
Geoffrey Hinton's Google Tech Talk linking machine learning to biology, explaining dropout as a form of model averaging and why sexual reproduction and stochastic spiking neurons may prevent overfitting.
Robocalypse Now: Using Deep Learning to Combat Cheating in Counter-Strike: Global Offensive
Valve's John McDonald presents at GDC 2018 on how Valve uses deep learning (VACnet) to detect cheaters in Counter-Strike: Global Offensive.
Why Greatness Cannot Be Planned — Kenneth Stanley
A long Machine Learning Street Talk interview with Kenneth Stanley, creator of NEAT and novelty search, about his book on why greatness cannot be planned.
Lecture 01 - The Learning Problem
The first lecture of Caltech's CS 156 Machine Learning course by Yaser Abu-Mostafa, introducing the learning problem and supervised, unsupervised and reinforcement learning.
Future Computers Will Be Radically Different (Analog Computing)
Veritasium explains why analog computing may suit AI workloads, covering old analog computers, The Analog Thing and Mythic's analog chips that use flash memory for neural network inference.
Uppestcase and Lowestcase Letters [advances in derp learning]
Tom Scott-style absurdist research by tom7 for SIGBOVIK 2021, using machine learning to discover letterforms that are 'uppercaser' than uppercase A and lowercaser than lowercase.
Let's build the GPT Tokenizer
Andrej Karpathy builds the GPT tokenizer from scratch, implementing byte pair encoding along with encode and decode.