Neural Networks for Machine Learning (Coursera Video Lectures) [YKG]

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Added on July 16, 2016 by YKTheDemonin Movies
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Neural Networks for Machine Learning (Coursera Video Lectures) [YKG] (Size: 884.52 MB)
 1 - 1 - Why do we need machine learning- [13 min].mp415.05 MB
 1 - 2 - What are neural networks- [8 min].mp49.76 MB
 1 - 3 - Some simple models of neurons [8 min].mp49.26 MB
 1 - 4 - A simple example of learning [6 min].mp46.57 MB
 1 - 5 - Three types of learning [8 min].mp48.96 MB
 10 - 1 - Why it helps to combine models [13 min].mp415.12 MB
 10 - 2 - Mixtures of Experts [13 min].mp414.98 MB
 10 - 3 - The idea of full Bayesian learning [7 min].mp48.39 MB
 10 - 4 - Making full Bayesian learning practical [7 min].mp48.13 MB
 10 - 5 - Dropout [9 min].mp49.69 MB
 11 - 1 - Hopfield Nets [13 min].mp414.65 MB
 11 - 2 - Dealing with spurious minima [11 min].mp412.77 MB
 11 - 3 - Hopfield nets with hidden units [10 min].mp411.31 MB
 11 - 4 - Using stochastic units to improv search [11 min].mp411.76 MB
 11 - 5 - How a Boltzmann machine models data [12 min].mp413.28 MB
 12 - 1 - Boltzmann machine learning [12 min].mp414.03 MB
 12 - 2 - OPTIONAL VIDEO- More efficient ways to get the statistics [15 mins].mp416.93 MB
 12 - 3 - Restricted Boltzmann Machines [11 min].mp412.68 MB
 12 - 4 - An example of RBM learning [7 mins].mp48.71 MB
 12 - 5 - RBMs for collaborative filtering [8 mins].mp49.53 MB
 13 - 1 - The ups and downs of back propagation [10 min].mp411.83 MB
 13 - 2 - Belief Nets [13 min].mp414.86 MB
 13 - 3 - Learning sigmoid belief nets [12 min].mp413.59 MB
 13 - 4 - The wake-sleep algorithm [13 min].mp415.68 MB
 14 - 1 - Learning layers of features by stacking RBMs [17 min].mp420.07 MB
 14 - 2 - Discriminative learning for DBNs [9 mins].mp411.29 MB
 14 - 3 - What happens during discriminative fine-tuning- [8 mins].mp410.17 MB
 14 - 4 - Modeling real-valued data with an RBM [10 mins].mp411.2 MB
 14 - 5 - OPTIONAL VIDEO- RBMs are infinite sigmoid belief nets [17 mins].mp419.44 MB
 15 - 1 - From PCA to autoencoders [5 mins].mp49.68 MB

Description

About the Course
Neural networks use learning algorithms that are inspired by our understanding of how the brain learns, but they are evaluated by how well they work for practical applications such as speech recognition, object recognition, image retrieval and the ability to recommend products that a user will like. As computers become more powerful, Neural Networks are gradually taking over from simpler Machine Learning methods. They are already at the heart of a new generation of speech recognition devices and they are beginning to outperform earlier systems for recognizing objects in images. The course will explain the new learning procedures that are responsible for these advances, including effective new proceduresr for learning multiple layers of non-linear features, and give you the skills and understanding required to apply these procedures in many other domains.

This YouTube video gives examples of the kind of material that will be in the course, but the course will present this material at a much gentler rate and with more examples.

Recommended Background
Programming proficiency in Matlab, Octave or Python. Enough knowledge of calculus to be able to differentiate simple functions. Enough knowledge of linear algebra to understand simple equations involving vectors and matrices. Enough knowledge of probability theory to understand what a probability density is.

Course Format
The class will consist of lecture videos, which are between 5 and 15 minutes in length. These contain 1-3 integrated quiz questions per video. There will also be standalone homework that is not part of video lectures, optional programming assignments, and a (not optional) final test.

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Neural Networks for Machine Learning (Coursera Video Lectures) [YKG]