Conditional Generative Adversarial Nets | M. Mirza, S. Osindero | 2014

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.

Studying Generative Adversarial Networks (GANs)

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]

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]

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 […]