Coursera - Neural Networks for Machine Learning (University of Toronto)

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Coursera - Neural Networks for Machine Learning (University of Toronto)
WEBRip | English | MP4 + PDF slides | 960 x 540 | AVC ~25.2 kbps | 15 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | ~17 hours | 979 MB
Genre: eLearning Video / Artificial Neural Network, Machine Learning (ML) Algorithms

Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

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.

Lecture 1: Introduction
Lecture 2: The Perceptron learning procedure
Lecture 3: The backpropagation learning proccedure
Lecture 4: Learning feature vectors for words
Lecture 5: Object recognition with neural nets
Lecture 6: Optimization: How to make the learning go faster
Lecture 7: Recurrent neural networks
Lecture 8: More recurrent neural networks
Lecture 9: Ways to make neural networks generalize better
Lecture 10: Combining multiple neural networks to improve generalization
Lecture 11: Hopfield nets and Boltzmann machines
Lecture 12: Restricted Boltzmann machines (RBMs)
Lecture 13: Stacking RBMs to make Deep Belief Nets
Lecture 14: Deep neural nets with generative pre-training
Lecture 15: Modeling hierarchical structure with neural nets
Lecture 16: Recent applications of deep neural nets

General
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