Digital Signal Processing | Coursera

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 […]

Computational Neuroscience | Coursera

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 […]

Dynamics and Cognitive Models | MS in CogSci

Lecture 1 | Introduction “Freud was inspired by the theory of thermodynamics and used the term psychodynamics to describe the processes of the mind as flows of psychological energy (libido or psi) in an organically complex brain.” [Psychodynamics – Wikipedia] Lecture 2 | Linear models What is a linear model? If the derivative of a […]

Seminar in Methodology on Experimental Psychology (Fundamentals and Applications of Cognitive Modeling) | MS in CogSci

Brief Information Name (en) :?Seminar in Methodology on Experimental Psychology?(Fundamentals and Applications of Cognitive Modeling) Name (ko) : 실험심리방법론세미나?(인지모델링의 기초와 응용) Lecturer : Koh, Sungryong 고성룡 Semester : 2018 Fall Major?: MS, Cognitive Science Textbook Busemeyer, J. R., & Diederich, A. (2010). Cognitive modeling. Sage. Syllabus : 2018-2_Seminar-in-Methodology-on-Experimental-Psychology.pdf In?short To learn cognitive modeling and its […]

Sequence Modeling | Deep Learning Specialization | Coursera

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 […]

Convolutional Neural Networks | Deep Learning Specialization | Coursera

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 […]

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 […]