CS231n: Convolutional Neural Networks for Visual Recognition | Course

Lecture 6 | Training Neural Networks I Sigmoid Problems of the sigmoid activation function Problem 1: Saturated neurons kill the gradients. Problem 2: Sigmoid outputs are not zero-centered. Suppose a given feed-forward neural network has hidden layers and all activation functions are sigmoid. Then, except the first layer, the other layers get only positive inputs. […]

Minds and Machines (24.09x) | edX

Brief Summary Course title: Minds and Machines [HOME] Platform: edX Duration: 15 weeks Instructors: Alex Byrne,?Chair of Philosophy Section, MIT Ryan Doody,?PhD in Philosophy & Linguistics,?MIT Short summary of this course An introduction to philosophy of mind, exploring consciousness, reality, AI, and more. The most in-depth philosophy course available online. About this course What is […]

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]

Studying Tensorflow

References LearningTensorFlow.com G?ron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems.?O’Reilly Media [Amazon] Siraj Raval (2016. 8. 19.). TensorFlow in 5 Minutes. YouTube. [YouTube] TensorBoard: 그래프 시각화 [LINK] Cifar-10 CNN implementation using TensorFlow library with 24% error [GitHub] Q&A In TensorFlow, what is the difference between […]

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 […]