1636212844_hqdefault.jpg
281
01:16:40

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 […]
1636208812_hqdefault.jpg
169
01:15:55

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, […]
1636205120_hqdefault.jpg
168
01:16:38

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 […]
1636201456_hqdefault.jpg
245
01:17:00

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, […]
1636197786_hqdefault.jpg
529
01:13:06

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 […]
1636194099_hqdefault.jpg
348
01:17:18

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 […]
1636190425_hqdefault.jpg
335
01:20:40

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, […]
1636186763_hqdefault.jpg
254
01:14:57

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 […]
1636183107_hqdefault.jpg
603
01:14:23

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 […]
1636179308_hqdefault.jpg
0

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 […]
1636175638_hqdefault.jpg
299
01:12:56

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 […]
1636171979_hqdefault.jpg
0

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 […]
1636168331_hqdefault.jpg
0

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 […]
1636164677_hqdefault.jpg
518
01:15:45

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, […]
1636161021_hqdefault.jpg
0

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 […]
1636157274_hqdefault.jpg
795
01:15:31

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 […]
1636153568_hqdefault.jpg
954
01:13:07

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 […]
1636149907_hqdefault.jpg
0

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 […]
1636146238_hqdefault.jpg
0

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 […]
1636142601_hqdefault.jpg
14.97K
01:08:40

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 […]
1634726602_hqdefault.jpg
23
07:05

Introduction to Neural Networks for Java(Class 3/16, Part 3/5) – hopfield example

Learn Neural Net Programming: http://www.heatonresearch.com/course/intro-neural-nets-java In class session 3, part 3 we will look at some examples of Hopfield Neural Networks. We will see a simple console Hopfield application, as well as a graphic pattern recognition program, and finally an application that shows the weight matrix as the application is trained. Artificial intelligence online course […]
1634666826_hqdefault.jpg
30
07:52

Introduction to Neural Networks for C#(Class 3/16, Part 3/5) – hopfield example

Learn Neural Net Programming: http://www.heatonresearch.com/course/intro-neural-nets-cs In class session 3, part 3 we will look at some examples of Hopfield Neural Networks. We will see a simple console Hopfield application, as well as a graphic pattern recognition program, and finally an application that shows the weight matrix as the application is trained. Artificial intelligence online course […]
1629416649_maxresdefault.jpg
65
01:02:33

Facebook Research – Unsupervised Translation of Programming Languages

In this episode of Machine Learning Street Talk Dr. Tim Scarfe, Yannic Kilcher and Connor Shorten spoke with Marie-Anne Lachaux, Baptiste Roziere and Dr. Guillaume Lample from Facebook Research (FAIR) in Paris. They recently released the paper “Unsupervised Translation of Programming Languages” which was an exciting new approach to learned translation of programming languages (learned […]