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]

CS231n: Convolutional Neural Networks for Visual Recognition | Course

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. […]

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]

Deep Learning | Udacity

Brief Information Instructor: Vincent Vanhoucke (Principal Scientist at Google Brain) Flatform: Udacity Course homepage:–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 6: […]

Deep Learning by I. Goodfellow, Y. Bengio and A. Courville

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  

Studying ‘Deep Learning’

References Lectures Hinton, G. (2013) Neural Networks for Machine Learning. Coursera Deep Learning Nanodegree Foundations. Udacity CS231n: Convolutional Neural Networks for Visual Recognition. Stanford University CS224d: Deep Learning for Natural Language Processing. Stanford University CS 294-131: Special Topics in Deep Learning. UC Berkeley CS 294: Deep Reinforcement Learning, Spring 2017. UC Berkeley Vanhoucke, V.. Deep Learning. Udacity Books Goodfellow, […]

How to Install Caffe

Install Caffe on Linux Ubuntu 16.04 My Configurations Linux: Ubuntu 16.04 LTS Anaconda: Anaconda 2 (64-bit), CPU or GPU: CPU Install Caffe 1. Install CUDA 8 (Optional. I failed this installation.) Go to the CUDA download page( ‘ Go to the directory where the downloaded file is located.

I got an error. Hit CTRL+ALT+F1 […]

Machine Learning | by Andrew Ng | Coursera

Brief Information Course name: Machine Learning Online platform: Coursera Lecturer: Andrew Ng in Stanford University Duration: 2016-12-26 ~ 03-12 (11 weeks) I started on 2017-01-09. Course information Record Grade Achieved: 99.6% Grades in detail: grade_machine_learning Certificate Key Words Lectures Week 1 Welcome to Machine Learning! This week, we introduce the core idea of teaching a computer to […]