Coursera - Neural Networks for Machine Learning

MP4 | AVC 29kbps | English | 960x540 | 15fps | 16h 26mins | AAC stereo 128kbps | 920 MB

Genre: Video Training

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. 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