MIT 6.0002 11. Introduction to Machine Learning
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MIT 6.0002 11. Introduction to Machine Learning

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016View the complete course: http://ocw.mit.edu/6-0002F16Instructor: Eric Grimson In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors. License: Creative Commons BY-NC-SAMore information at http://ocw.mit.edu/termsMore courses at http://ocw.mit.edu
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#55 Self-Supervised Vision Models (Dr. Ishan Misra – FAIR).

Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at […]
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Lecture 6 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the applications of naive Bayes, neural networks, and support vector machine. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive […]
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Lecture 17 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses the topic of reinforcement learning, focusing particularly on continuous state MDPs, discretization, and policy and value iterations. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, […]
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Two Minute Papers: Ubisoft’s New AI Predicts the Future of Virtual Characters! 🐺

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper “SuperTrack – Motion Tracking for Physically Simulated Characters using Supervised Learning” is available here: https://static-wordpress.akamaized.net/montreal.ubisoft.com/wp-content/uploads/2021/11/24183638/SuperTrack.pdf SuperTrack – Motion Tracking for Physically Simulated Characters using Supervised Learning ❤️ Watch these videos in early access on our Patreon page or join […]
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SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (Mathilde Caron)

This week Dr. Tim Scarfe, Yannic Lightspeed Kicher, Sayak Paul and Ayush Takur interview Mathilde Caron from Facebook Research (FAIR). We discuss Mathilde’s paper which she wrote with her collaborators “SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments” @ https://arxiv.org/pdf/2006.09882.pdf This paper is the latest unsupervised contrastive visual representations algorithm and has a […]
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Lecture 7 | Machine Learning (Stanford)

Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/zJX/ Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on optimal margin classifiers, KKT conditions, and SUM duals. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, […]
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[Virtual Community Meetup] December Lightning Talks

Lightning Talks are a series of virtual events featuring the PyTorch Lightning community. They are quick talks that give you insights on developments, projects and use cases the community is working on. We hope you get a bolt of inspiration! Agenda: Welcome by Eden Afek, Product Manager at PyTorch Lightning Talk #1 – “Sharded Model […]
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Lecture 18 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses state action rewards, linear dynamical systems in the context of linear quadratic regulation, models, and the Riccati equation, and finite horizon MDPs. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics […]
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061: Interpolation, Extrapolation and Linearisation (Prof. Yann LeCun, Dr. Randall Balestriero)

We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST Yann LeCun thinks that it’s specious to say neural network models are interpolating because in high dimensions, everything is extrapolation. Recently Dr. Randall Bellestrerio, Dr. Jerome Pesente and prof. Yann LeCun released their paper learning in high dimensions always amounts to […]
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Neural Architecture Search and Google’s New AutoML Zero with Quoc Le – #366

Today we’re super excited to share our recent conversation with Quoc Le, a research scientist at Google, on the Brain team. Quoc has been very busy recently with his work on Google’s AutoML Zero, which details significant advances in automated machine learning that can “automatically discover complete machine learning algorithms just using basic mathematical operations […]
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Lecture 8 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and […]
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The future will not be supervised… | Machine Learning Monthly July 2020

This month has been all about GPT3 and self-supervised learning. GPT3 is the latest in language modelling from OpenAI and self-supervised learning seeks to understand the inherent underlying patterns of the data itself which can then be used for more specific tasks later. It can result in up to 1000x fewer labels being required and […]
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Lecture 19 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on the debugging process, linear quadratic regulation, Kalmer filters, and linear quadratic Gaussian in the context of reinforcement learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, […]
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#063 – Prof. YOSHUA BENGIO – GFlowNets, Consciousness & Causality

We are now sponsored by Weights and Biases! Please visit our sponsor link: http://wandb.me/MLST to get started creating a centralised, system of record for your team’s machine learning work. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud For Yoshua Bengio, GFlowNets are the most exciting thing on the horizon of Machine Learning today. He believes they can solve previously […]
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Self-Supervised Learning of Image Features with SwAV (with author Mathilde Caron)

In this video, author Mathilde Caron features “SWaV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments”. SwAV is the latest paper from FAIR and Inria to post state of the art results in self-supervised learning. The paper combines ideas from contrastive learning and clustering based approaches to train image representations. The swapped prediction problem […]
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Lecture 9 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into learning theory, covering bias, variance, empirical risk minimization, union bound and Hoeffding’s inequalities. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement […]
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DeepMind x UCL | Deep Learning Lectures | 4/12 | Advanced Models for Computer Vision

Following on from the previous lecture, DeepMind Research Scientist Viorica Patraucean introduces classic computer vision tasks beyond image classification (object detection, semantic segmentation, optical flow estimation) and describes state of the art models for each, together with standard benchmarks. She discusses similar models for video processing for tasks like action recognition, tracking, and the associated […]
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Lecture 20 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses POMDPs, policy search, and Pegasus in the context of reinforcement learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive […]