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 […]
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 […]
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. […]
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]
[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 […]
Chapter 1 (h3) Section 1.1 (h4) Section 1.1.1 (h5) Theme (h6) Chapter 1 Introduction The performance of machine learning algorithms depends heavily on the representation of the data. The representation consists of features. Representation learning is machine learning to learn efficient representation of the given data. Deep learning so