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Neural Networks and Learning Machines. 3rd Ed. Simon O. Haykins. Pearson. 2008

Chapter 8. Principal-Components Analysis 8.1 Introduction 8.2 Principles of Self-Organization Principle 1. Self-Amplification Principle 2. Competition Principle 3. Cooperation Principle 4. Structural Information 8.3 Self-Organized Feature Analysis 8.4 Principal-Components Analysis: Perturbation Theory 8.5 Hebbian-Based maximum Eigenfilter 8.6 Hebbian-Based Principal Components Analysis 8.7 Case Study: Image Coding 8.8 Kernel Principal-Components Analysis 8.9 Basic Issues Involved in […]

Computational Neuroscience | Course | MS CogSci

Range 8.1~8.7 9.1~9.10 10.1~10.14 10.19~10.21 Chapter 8. Principal-Components Analysis 8.1. Introduction Self-organized learning Self-organized learning is a type of unsupervised learning. locality of learning 8.2. Principles of Self-Organization Principle 1: self-amplification The following rule is based on Hebb’s postulate of learning. If two neurons of a synapse are activated simultaneously, then synaptic strength is selectively […]

Sequence to Sequence Learning with Neural Networks | Summary

References Ilya Sutskever, Oriol Vinyals, Quoc V. Le (2014). “Sequence to Sequence Learning with Neural Networks”. NIPS 2014: 3104-3112. [PDF] Sequence-to-Sequence Models. TensorFlow [LINK] The official tutorial for sequence-to-sequence models. Seq2seq Library (contrib). Tensorflow [LINK] Translation with a Sequence to Sequence Network and Attention. PyTorch. [LINK]

Intro to Psychology | Udacity

[latexpage] Brief Information Instructors: Susan Snycerski (Assistant Professor, San Jose State University) Greg Feist?(Professor, San Jose State University) Lauren Castellano (Udacity) Flatform: Udacity Course homepage:?https://www.udacity.com/course/intro-to-psychology–ps001 Duration 2017-08-24~present: (blank) About this course Introduction to Psychology is a journey through all of the major psychological concepts and principles. The knowledge gained from this course will allow students […]

Heroes of Deep Learning Interviews by Andrew Ng

Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton [40:23] Heroes of Deep Learning: Andrew Ng interviews Ian Goodfellow [14:56] Heroes of Deep Learning: Andrew Ng interviews Yoshua Bengio [25:49] Heroes of Deep Learning: Andrew Ng interviews Pieter Abbeel [16:04] Heroes of Deep Learning: Andrew Ng interviews Head of Baidu Research, Yuanqing Lin [13:37] Heroes […]

Deep Learning | Udacity

[latexpage] Brief Information Instructor:?Vincent Vanhoucke (Principal Scientist at Google Brain) Flatform: Udacity Course homepage:?https://www.udacity.com/course/deep-learning–ud730 Duration 2017-08-24~25:?Took Lesson 1, 3-7 without programming assignments. Course Overview Lesson 1: From Machine Learning to Deep Learning Lesson 2: Assignment: notMNIST Lesson 3: Deep Neural Networks Lesson 4: Convolutional Neural Networks Lesson 5: Deep Models for Text and Sequences Lesson […]

Deep Learning by I. Goodfellow, Y. Bengio and A. Courville

Chapter 1 (h3) Section 1.1 (h4) Section 1.1.1 (h5) Theme (h6) Chapter 1 Introduction The performance of machine learning algorithms depends heavily on the representation of the data. The representation consists of features. Representation learning is machine learning to learn efficient representation of the given data. Deep learning so