Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization | Deep Learning Specialization | Coursera

Brief information Course name: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Instructor: Andrew Ng Institution: deeplearning.ai Media: Coursera Specialization: Deep Learning Duration: 3 weeks About this Course This course will teach you the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand […]

Structuring Machine Learning Projects | Deep Learning Specialization | Coursera

Brief information Course name: Structuring Machine Learning Projects Instructor: Andrew Ng Institution: deeplearning.ai Media: Coursera Specialization: Deep Learning Duration: 2 weeks About this Course You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team’s work, this […]

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. […]

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 […]

Computational Neuroscience | Course | MS CogSci

Range 8.1~8.7 9.1~9.10 10.1~10.14 10.19~10.21 Chapter 8. Principal-Components Analysis 8.1. Introduction Self-organized learning Self-organized learning is a type of unsupervised learning. locality of learning 8.2. Principles of Self-Organization Principle 1: self-amplification The following rule is based on Hebb’s postulate of learning. If two neurons of a synapse are activated simultaneously, then synaptic strength is selectively […]

Intro to Psychology | Udacity

Brief Information Instructors: Susan Snycerski (Assistant Professor, San Jose State University) Greg Feist (Professor, San Jose State University) Lauren Castellano (Udacity) Flatform: Udacity Course homepage: https://www.udacity.com/course/intro-to-psychology–ps001 Duration 2017-08-24~present: (blank) About this course Introduction to Psychology is a journey through all of the major psychological concepts and principles. The knowledge gained from this course will allow students to […]

Deep Learning | Udacity

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 6: […]

Introduction to the Philosophy of Cognitive Sciences | Coursera

Brief Information Course name: Philosophy and the Sciences: Introduction to the Philosophy of Cognitive Sciences Online platform: Coursera Lecturer: Suilin Lavelle, Kenny Smith, Mark Sprevak, David Carmel, Andy Clark & Barbara Webb in The University of Edinburgh Duration: 2017-03-20 ~ 04-24 (4 weeks) I started on 2017-03-24. Course information Record Grade Achieved: 100% Grades in detail: [LINK] […]

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 […]

Differential Geometry | MA

Brief Information Name : Differential Geometry 미분기하학 Lecturer : 장정환 Jang Jeonghwan Semester : 2016 Fall Major : BS, Mathematics Textbook O’Neill, B. (2006) Elementary Differential Geometry. Revised 2nd Ed. Academic Press Syllabus : Syllabus_2016-5-2__Differential Geometry.pdf In short The geometry of curves and surfaces Summary