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Two Minute Papers: Can an AI Learn The Concept of Pose And Appearance? πŸ‘±β€β™€οΈ

❀️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers πŸ“ The paper “HoloGAN: Unsupervised learning of 3D representations from natural images” is available here: https://www.monkeyoverflow.com/#/hologan-unsupervised-learning-of-3d-representations-from-natural-images/ ❀️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers πŸ™ We would like to thank our generous Patreon supporters who make Two Minute Papers possible: […]
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Four Key Tools for Robust Enterprise NLP with Yunyao Li – #537

Today we’re joined by Yunyao Li, a senior research manager at IBM Research. Yunyao is in a somewhat unique position at IBM, addressing the challenges of enterprise NLP in a traditional research environment, while also having customer engagement responsibilities. In our conversation with Yunyao, we explore the challenges associated with productizing NLP in the enterprise, […]
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Can Self-Driving Cars Learn Depth Perception? 🚘

❀️ Check out Weights & Biases here and sign up for a free demo here: https://www.wandb.com/papers The showcased instrumentation post is available here: https://app.wandb.ai/stacey/sfmlearner/reports/See-3D-from-Video%3A-Depth-Perception-for-Self-Driving-Cars–Vmlldzo2Nzg2Nw πŸ“ The paper “Unsupervised Learning of Depth and Ego-Motion from Video” is available here: https://people.eecs.berkeley.edu/~tinghuiz/projects/SfMLearner/ Β πŸ™ We would like to thank our generous Patreon supporters who make Two Minute Papers possible: […]
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17:27

Exploring the Intersection of Unsupervised Learning and Reinforcement Learning – Clips – #476

In this clip we explore Pieter Abbeel’s recent interests in the intersection of unsupervised learning and reinforcement learning. Check out the full interview at https://youtu.be/69paLneM6LU. Subscribe: Apple Podcasts: https://tinyurl.com/twimlapplepodcast Spotify: https://tinyurl.com/twimlspotify Google Podcasts: https://podcasts.google.com/?feed=aHR0cHM6Ly90d2ltbGFpLmxpYnN5bi5jb20vcnNz RSS: https://twimlai.libsyn.com/rss Full episodes playlist: Subscribe to our Youtube Channel: https://www.youtube.com/channel/UC7kjWIK1H8tfmFlzZO-wHMw?sub_confirmation=1 Podcast website: Sign up for our newsletter: Newsletter Sign-Up Check […]
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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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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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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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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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529
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Lecture 16 | 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 MDPs, value functions, and policy and value iteration. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
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Lecture 15 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on principal component analysis (PCA) and independent component analysis (ICA) in relation to unsupervised machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
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Lecture 14 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his discussion on factor analysis and expectation-maximization steps, and continues on to discuss principal component analysis (PCA). This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, […]
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Lecture 13 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on expectation-maximization in the context of the mixture of Gaussian and naive Bayes models, as well as factor analysis and digression. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include […]
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603
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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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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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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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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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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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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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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 5 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on generative learning algorithms and Gaussian discriminative analysis and their applications in machine learning. 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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Lecture 4 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton’s method, exponential families, and generalized linear models and how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
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Lecture 3 | Machine Learning (Stanford)

Help us caption and translate this video on Amara.org: http://www.amara.org/en/v/BGwS/ Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng delves into locally weighted regression, probabilistic interpretation and logistic regression and how it relates to machine learning. This course provides a broad introduction to machine learning and […]
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Lecture 2 | Machine Learning (Stanford)

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning. This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning […]
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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 […]