Lecture 2: Markov Decision Processes | Reinforcement Learning | David Silver | Course

1. Markov Process / Markov chain 1.1. Markov process A Markov process or Markov chain is a tuple such that is a finite set of states, and is a transition probability matrix. In a  Markov process, the initial state should be given. How do we choose the initial state is not a role of the Markov process. 1.2. State […]

Reinforcement Learning | David Silver | Course

Brief information Instructor: David Silver Course homepage: [LINK] Video lecture list: [LINK] Lecture schedule Lecture 1: Introduction to Reinforcement Learning Lecture 2: Markov Decision Processes Lecture 3: Planning by Dynamic Programming Lecture 4: Model-Free Prediction Lecture 5: Model-Free Control Lecture 6: Value Function Approximation Lecture 7: Policy Gradient Methods Lecture 8: Integrating Learning and Planning […]

Inception Module | Summary

References Udacity (2016. 6. 6.). Inception Module. YouTube. [LINK] Udacity (2016. 6. 6.). 1×1 Convolutions. YouTube. [LINK] Tommy Mulc (2016. 9. 25.). Inception modules: explained and implemented. [LINK] Szegedy et al. (2015). Going Deeper with Convolutions. CVPR 2015. [arXiv] Summary History The inception module was first introduced in GoogLeNet for ILSVRC’14 competition. Key concept Let a convolutional network decide […]

Batch Normalization | Summary

References Sergey Ioffe, Christian Szegedy (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ICML 2015. [ICML][arXiv] Lecture 6: Training Neural Networks, Part 1. CS231n:Convolutional Neural Networks for Visual Recognition. 48:52~1:04:39 [YouTube] Choung young jae (2017. 7. 2.). PR-021: Batch Normalization. Youtube. [YouTube] tf.nn.batch_normalization. Tensorflow. [LINK] Rui Shu (27 DEC 2016). A GENTLE […]

Convolutional Neural Networks | Study

  References L. Fei-Fei, Justin Johnson (Spring 2017)CS231n: Convolutional Neural Networks for Visual Recognition. [LINK] Jefkine (5 September 2016). Backpropagation In Convolutional Neural Networks. [LINK] Convnet: Implementing Convolution Layer with Numpy [LINK] CNN의 역전파(backpropagation) [LINK]

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]