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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#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 […]
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Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli – #560

Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of […]
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SimCLR with PyTorch Lightning- intro

If you are just getting into deep learning research, or want to deepen your knowledge on self-supervised learning latest practices, tune in to this series: https://www.youtube.com/watch?v=pDJx8i3jenA&list=PLaMu-SDt_RB4k8VXiB3hOdsn0Y3GoXo1k William Falcon, PyTorch Lightning founder, and Ananya Harsh Ja, Lightning research engineer, deep dive into simCLR (“A Simple Framework for Contrastive Learning of Visual Representations”), self-supervised representation learning on […]
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Lecture 10 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture on learning theory by discussing VC dimension and model selection. 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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ResNets are back baby!!! …and they're gone | Machine Learning Monthly March 2021

Machine Learning Monthly covers the latest and greatest (but not always the latest) in machine learning of the previous month. This month we see things like multimodal modelling (modelling multiple sources of data types such as images combined with text) and self-supervised learning taking centre stage. Machine Learning Monthly March 2021 – https://zerotomastery.io/blog/machine-learning-monthly-march-2021/ EfficientNetV2 – […]
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Two Minute Papers: AI-Based 3D Pose Estimation: Almost Real Time!

📝 The paper “3D Human Pose Machines with Self-supervised Learning” and its source code is available here: https://arxiv.org/abs/1901.03798 http://www.sysu-hcp.net/3d_pose_ssl/ https://github.com/chanyn/3Dpose_ssl.git ❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: 313V, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, […]
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AI Enterprise Workflow Study Group – Course 4, Week 1

Hi Everyone, We’ve just completed Course 4: Machine Learning, Visual Recognition, and NLP, in the AI Enterprise Workflow Specialization study group. Course four was really interesting. Week 1 we reviewed model evaluation and performance metrics and week 2 we discussed building machine learning and deep learning models. Here’s a list of the topics covered in […]
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Reinforcement learning overview (Reinforcement learning with TensorFlow Agents)

Wei Wei, a Developer Advocate for TensorFlow, kicks off a new series on reinforcement learning where we explore how you can leverage TensorFlow Agents to build your own reinforcement learning agents. Wei explains how reinforcement learning can be used to train agents to make the best decisions when performing actions in environments to maximize rewards. […]
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Snorkel: A System for Fast Training Data Creation with Alex Ratner – TWiML Talk #270

Today we’re joined by Alex Ratner, Ph.D. student at Stanford. In our conversation, we discuss: • Snorkel, the open source framework that is the successor to Stanford’s Deep Dive project. • How Snorkel is used as a framework for creating training data with weak supervised learning techniques. • Multiple use cases for Snorkel, including how […]
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Lecture 11 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Bayesian statistics, regularization, digression-online learning, and the applications of machine learning algorithms. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning […]
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DeepMind x UCL | Deep Learning Lectures | 10/12 | Unsupervised Representation Learning

Unsupervised learning is one of the three major branches of machine learning (along with supervised learning and reinforcement learning). It is also arguably the least developed branch. Its goal is to find a parsimonious description of the input data by uncovering and exploiting its hidden structures. This is presumed to be more reminiscent of how […]
CoRL 2020, Spotlight Talk 465: Self-Supervised Learning of Scene-Graph Representations for Robotics
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CoRL 2020, Spotlight Talk 465: Self-Supervised Learning of Scene-Graph Representations for Robotics

**Self-Supervised Learning of Scene-Graph Representations for Robotic Sequential Manipulation Planning** Son Nguyen (University Stuttgart)*; Ozgur Oguz (Uni. of Stuttgart & Max Planck Inst. for Intelligent Systems ); Valentin Hartmann (University of Stuttgart); Marc Toussaint (Technische Universität Berlin) Publication: http://corlconf.github.io/paper_465/ **Abstract** We present a self-supervised representation learning approach for visual reasoning and integrate it into a […]
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Lecture 1 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting. 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 control. Recent […]
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Will there be Human-Level Artificial Intelligence by 2030?

Artificial Intelligence has been improving rapidly these past few years, and it’s now becoming obvious according to the top AI Scientists such as Yann LeCun that AI in 2030 will be almost unrecognizable compared to ones right now. They will be part of everyday lifes in the form of digital assistants and more. What other […]
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Self Supervised Learning

What is self-supervised learning? How do you learn without labeling your data? William Falcon, PyTorch Lightning founder, and Ananya Harsh Ja, Lightning research engineer, deep dive into simCLR (“A Simple Framework for Contrastive Learning of Visual Representations”), self-supervised representation learning on images. Start here for the intro: https://www.youtube.com/watch?v=pDJx8i3jenA&list=PLaMu-SDt_RB4k8VXiB3hOdsn0Y3GoXo1k In the next videos, we will go […]
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Lecture 12 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng discusses unsupervised learning in the context of clustering, Jensen’s inequality, mixture of Gaussians, and expectation-maximization. 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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Crash Course In Machine Learning Part 2 – What Is Supervised Learning

In part 2 of a crash course in machine learning, we get an overview of three big algorithms for supervised learning. Linear regression for calculating continuous variables Logistic regression and neural networks for calculating discrete outputs Learn how to turn deep reinforcement learning papers into code: Deep Q Learning: https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-OCT-21 Actor Critic Methods: https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-OCT-21 Curiosity […]