Proof that the Kernel of a Linear Transformation is a Subspace
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Proof that the Kernel of a Linear Transformation is a Subspace
From playlist Proofs
Determine the Kernel of a Linear Transformation Given a Matrix (R3, x to 0)
This video explains how to determine the kernel of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Introduction to the Kernel and Image of a Linear Transformation
This video introduced the topics of kernel and image of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Concept Check: Describe the Kernel of a Linear Transformation (Projection onto y=x)
This video explains how to describe the kernel of a linear transformation that is a projection onto the line y = x.
From playlist Kernel and Image of Linear Transformation
Determine a Basis for the Kernel of a Matrix Transformation (3 by 4)
This video explains how to determine a basis for the kernel of a matrix transformation.
From playlist Kernel and Image of Linear Transformation
Kernel of a group homomorphism
In this video I introduce the definition of a kernel of a group homomorphism. It is simply the set of all elements in a group that map to the identity element in a second group under the homomorphism. The video also contain the proofs to show that the kernel is a normal subgroup.
From playlist Abstract algebra
Generalized Conway Game of Life - SmoothLife3
The transfer function that is used to calculate every new timestep can be arbitrarily complex. The kernel does not change, just as in different variations of the Game of Life, where birth/death values change, but not the kernel.
From playlist SmoothLife
Some Theoretical Results on Model-Based Reinforcement Learning by Mengdi Wang
Program Advances in Applied Probability II (ONLINE) ORGANIZERS: Vivek S Borkar (IIT Bombay, India), Sandeep Juneja (TIFR Mumbai, India), Kavita Ramanan (Brown University, Rhode Island), Devavrat Shah (MIT, US) and Piyush Srivastava (TIFR Mumbai, India) DATE & TIME 04 January 2021 to
From playlist Advances in Applied Probability II (Online)
Find the Kernel of a Matrix Transformation (Give Direction Vector)
This video explains how to determine direction vector a line that represents for the kernel of a matrix transformation
From playlist Kernel and Image of Linear Transformation
From playlist Contributed talks One World Symposium 2020
From playlist Contributed talks One World Symposium 2020
Select Which Vectors are in the Kernel of a Matrix (2 by 3)
This video explains how to determine which vectors for a list are in the kernel of a matrix.
From playlist Kernel and Image of Linear Transformation
Unsupervised state embedding and aggregation towards scalable reinforcement learning - Mengdi Wang
Workshop on New Directions in Reinforcement Learning and Control Topic: Unsupervised state embedding and aggregation towards scalable reinforcement learning Speaker: Mengdi Wang Affiliation: Princeton University Date: November 7, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Kernel and Rich Regimes in Deep Learning - Nati Srebro
Workshop on Theory of Deep Learning: Where next? Topic: Kernel and Rich Regimes in Deep Learning Speaker: Nati Srebro Affiliation: TTIC Date: October 17, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Band-pass filtering and the filter-Hilbert method
The Hilbert transform produces uninterpretable results on broadband data. You will need to narrow-band filter the signal first. This video shows one method of computing an FIR filter and applying it to EEG data. Together with the Hilbert transform, this gives us the filter-Hilbert method.
From playlist OLD ANTS #4) Time-frequency analysis via other methods
Lecture 5: Equivariant CNNs II (Riemannian manifolds) - Maurice Weiler
Video recording of the First Italian School on Geometric Deep Learning held in Pescara in July 2022. Slides: https://www.sci.unich.it/geodeep2022/slides/CoordinateIndependentCNNs.pdf
From playlist First Italian School on Geometric Deep Learning - Pescara 2022
Alexander Bufetov: Determinantal point processes - Lecture 3
Abstract: Determinantal point processes arise in a wide range of problems in asymptotic combinatorics, representation theory and mathematical physics, especially the theory of random matrices. While our understanding of determinantal point processes has greatly advanced in the last 20 year
From playlist Probability and Statistics
Stanford Seminar - KUtrace 2020
Dick Sites February 5, 2020 Observation tools for understanding occasionally-slow performance in large-scale distributed transaction systems are not keeping up with the complexity of the environment. The same applies to large database systems, to real-time control systems in cars and airp
From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series
Linear Transformation: Which Vectors are in the Range of T and the Kernel of T?
This video explains how to determine if a given vector in the range / image and the kernel of linear transformation.
From playlist Kernel and Image of Linear Transformation