1.2.a Discrete-time signals Discrete-time signals Discrete-time signal:= A sequence of complex numbers Dimension = 1 (for now) Notation: where is an integer Two-sided sequences: is one-dimensional “time”. Analysis: Periodic measurement approach Discrete-time signals can be created by an analysis process where we take periodic measurements of a physical phenomenon. Synthesis: Stream of generated samples Delta […]
Brief information Instructors: Rajesh P. N. Rao, Adrienne Fairhall About this course: This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered […]
Course planning Week 1: Recurrent neural networks Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section. Lectures: Recurrent neural networks C4W1L01 Why sequence models C4W1L02 […]
Course Planning Week 1:?Foundations of convolutional neural networks Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems. Convolutional neural networks C4W1L01 Computer vision C4W1L02 Edge detection example C4W1L03 More edge detection C4W1L04 Padding C4W1L05 Strided convolutions C4W1L06 Convolutions over […]
Lecture Planning Week 1:?Introduction to Deep Learning Welcome to the Deep Learning Specialization C1W1L01 Welcome Introduction to Deep Learning C1W1L02 Welcome C1W1L03 What is a neural network? C1W1L04 Supervised Learning with Neural Networks C1W1L05 Why is Deep Learning taking off? C1W1L06 About this Course C1W1R1 Frequently Asked Questions C1W1L07 Course Resources C1W1R2 How to use […]
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 […]
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 […]
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] […]
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 […]
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 […]