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Introduction Conditional version of Generative Adversarial Nets (GAN) where both generator and discriminator are conditioned on some data y (class label or data from some other modality). Architecture Feed y into both the generator and discriminator as additional input layers such that y and input are combined in a joint hidden representation.
References Lecture 13: Generative Models. CS231n: Convolutional Neural Networks for Visual Recognition. Spring 2017. [SLIDE][VIDEO] Generative Adversarial Nets.?Goodfellow et al.. NIPS 2014. 2014. [LINK][arXiv] How to Train a GAN? Tips and tricks to make GANs work. Soumith Chintala. github. [LINK] The GAN Zoo.?Avinash Hindupur. github. [LINK]
1. Markov Process / Markov chain 1.1. Markov process A?Markov process?or?Markov chain?is a tuple $\langle S,P \rangle$ such that $S$ is a finite set of states, and $P$ 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 […]
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 […]
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References A Survival Guide to a PhD.?Andrej Karpathy blog.?Sep 7, 2016 [LINK] HOWTO: Get into grad school for science, engineering, math and computer science [LINK] 대학원생을 위한 지극히 개인적인 10가지 조언 [LINK] 논문 읽기 초보자를 위한 Literature survey (문헌 조사) 팁! [LINK] 석사와 박사 [LINK] 내가 대학원에서 생존한 방법 [LINK] 박사 과정을 통해 배운 것들 […]
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]
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. […]
Author:?Mor Harchol-Balter (Computer Science Department?Carnegie Mellon University) Last updated: 2014 1 Introduction This document is intended for people applying to Ph.D. programs in computer science or related areas. The author is a professor of computer science?at CMU, and has been involved in the Ph.D. admissions process at CMU, U.C.?Berkeley, and MIT. 2 Do I […]
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