References Reinforcement Learning – Algorithms [LINK] About Q-learning and SARSA REINFORCEMENT LEARNING PART 2: SARSA VS Q-LEARNING [LINK] About Q-learning and SARSA
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 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 […]
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 […]
There is no excerpt because this is a protected post.
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. […]