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 […]

Neural Network for Machine Learning | by Geoffrey Hinton | Coursera

Brief Information Course name : Neural Network for Machine Learning Lecturer : Geoffrey Hinton Duration: Syllabus Record Certificate Learning outcome About this course Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms […]

Kruskal’s Algorithm

Kruskal’s Algorithm Approach: greedy approach Purpose: to find a minimum spanning tree in a weighted graph Example   References Kruskal’s Algorithm | Essay Assignment | Seminar on Advanced Mathematics Spanning Tree | tutorialspoint Kruskal’s Spanning Tree Algorithm | tutorialspoint Prim’s Spanning Tree Algorithm | tutorialspoint


In practice, quicksort outperforms merge sort, and it significantly outperforms selection sort and insertion sort. The average-case running time of quicksort is as good as mergesort’s. That’s because the constant factor hidden in the big-Θ notation for quicksort makes mergesort quite faster than mergesort. Not using additional storage. pivot, split point Example References Overview of quicksort | […]