1:08:40machine learning fundamentalsCourse
Lecture 1 | Machine Learning (Stanford)
Stanford
11 HN recommenders
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- Category: AI, Machine Learning & Data
Results for “course”
18 videos1:08:40
1:28:13reinforcement learningCourse
RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning
Google DeepMind
9 HN recommenders
1:21:28
5:56kalman filteringCourse
Special Topics - The Kalman Filter (1 of 55) What is a Kalman Filter?
Michel van Biezen
5 HN recommenders
48:32gpt-4 capabilitiesLecture
Sparks of AGI: early experiments with GPT-4
Sebastien Bubeck
7 HN recommenders
47:15AI and neural networksLecture
Will AI outsmart human intelligence? - with 'Godfather of AI' Geoffrey Hinton
The Royal Institution
3 HN recommenders
1:11:41natural language processingLecture
Lecture 1 | Natural Language Processing with Deep Learning
Stanford University School of Engineering
3 HN recommenders
1:29:43hamiltonian monte carloLecture
Efficient Bayesian inference with Hamiltonian Monte Carlo -- Michael Betancourt (Part 1)
MLSS Iceland 2014
3 HN recommenders
1:01:51information theoryLecture
Lecture 1: Introduction to Information Theory
Jakob Foerster
3 HN recommenders
1:22:38transformers and attentionLecture
CS480/680 Lecture 19: Attention and Transformer Networks
Pascal Poupart
3 HN recommenders
1:22:56practical deep learningCourse
Practical Deep Learning for Coders: Lesson 1
Jeremy Howard
2 HN recommenders
1:13:22self-driving carsLecture
Sacha Arnoud, Director of Engineering, Waymo - MIT Self-Driving Cars
Lex Fridman
3 HN recommenders
1:06
1:05:18
1:10:58deep learning hardwareLecture
Trends in Deep Learning Hardware: Bill Dally (NVIDIA)
Paul G. Allen School
3 HN recommenders
57:57computer vision deep learningLecture
Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition
Stanford University School of Engineering
2 HN recommenders
1:19:08convolutional neural networksCourse
CS231n Winter 2016: Lecture1: Introduction and Historical Context
Andrej Karpathy
2 HN recommenders
1:21Sources and selection
Machine learning involves both the models and the data and systems around them. This collection spans neural networks, language models, computer vision, data engineering and the broader consequences of AI. Students can begin with an introductory topic; practitioners can use the subject links to find a more specific technical discussion.
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