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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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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 […]
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01:24:39

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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10:42

GANS for Semi-Supervised Learning in Keras (7.4)

Generative Adversarial Networks (GANs) are not just for whimsical generation of computer images, such as faces. GANs can also be an effective means of dealing with semi-supervised learning, where only some of the data are labeled. This video introduces semi-supervised learning for Keras. Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_07_4_gan_semi_supervised.ipynb Course Homepage: https://sites.wustl.edu/jeffheaton/t81-558/ Follow Me/Subscribe: https://www.youtube.com/user/HeatonResearch https://github.com/jeffheaton […]
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05:31

Torchtext 0.4 with Supervised Learning Datasets: A Quick Introduction by George Zhang

Torchtext is a domain library for PyTorch that provides the fundamental components for working with text data, such as commonly used datasets and basic preprocessing pipelines, designed to accelerate natural language processing (NLP) research and machine learning (ML) development. George Zhang, a PyTorch Software Engineer, walks through the torchtext 0.4 release at the PyTorch Summer […]
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01:30:19

Trends in Computer Vision with Amir Zamir – #338

Today we close out AI Rewind 2019 joined by Amir Zamir, who recently began his tenure as an Assistant Professor of Computer Science at the Swiss Federal Institute of Technology. Amir joined us back in 2018 to discuss his CVPR Best Paper winner, and in today’s conversation, we continue with the thread of Computer Vision. […]
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16:15

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 […]
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01:44:41

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 […]
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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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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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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 […]