If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.
From playlist Machine Learning
Introduction to Multi-Agent Reinforcement Learning
Learn what multi-agent reinforcement learning is and some of the challenges it faces and overcomes. You will also learn what an agent is and how multi-agent systems can be both cooperative and adversarial. Be walked through a grid world example to highlight some of the benefits of both de
From playlist Reinforcement Learning
Kernels Introduction - Practical Machine Learning Tutorial with Python p.29
In this machine learning tutorial, we introduce the concept of Kernels. Kernels can be used with the Support Vector Machine in order to take a new perspective and hopefully allow us to translate into further dimensions in order to find a linearly separable case. https://pythonprogramming
From playlist Machine Learning with Python
Linear Programming, Lecture 16. More examples on dual problems. Duality Theorem.
Oct 18, 2016. Penn State University.
From playlist Math484, Linear Programming, fall 2016
Why Kernels - Practical Machine Learning Tutorial with Python p.30
Once we've determined that we can use Kernels, the next question is of course why would we bother using kernels when we can use some other function to transform our data into more dimensions. The point of using Kernels is to be able to perform a calculation (inner product in this case) in
From playlist Machine Learning with Python
Transformer (Attention is all you need)
understanding Transformer with its key concepts (attention, multi head attention, positional encoding, residual connection label smoothing) with example. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
what is linear and non linear in machine learning, deep learning
what is linear and non linear in machine learning and deep learning? you will have clear understanding after watching this video. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning
This lecture gives an overview of the main categories of machine learning, including supervised, un-supervised, and semi-supervised techniques, depending on the availability of expert labels. We also discuss the different methods to handle discrete versus continuous labels. Book websit
From playlist Intro to Data Science
Lesson 04_10 Multiple dispatch
When functions are created, a whole family actual exist, each called up for use based on the type of the values that are passed as ordered arguments.
From playlist The Julia Computer Language
Convolution as spectral multiplication
This video lesson is part of a complete course on neuroscience time series analyses. The full course includes - over 47 hours of video instruction - lots and lots of MATLAB exercises and problem sets - access to a dedicated Q&A forum. You can find out more here: https://www.udemy.
From playlist NEW ANTS #3) Time-frequency analysis
Depthwise Separable Convolution - A FASTER CONVOLUTION!
In this video, I talk about depthwise Separable Convolution - A faster method of convolution with less computation power & parameters. We mathematically prove how it is faster, and discuss applications where it is used in modern research. If you liked that video, hit that like button. If
From playlist Deep Learning Research Papers
Lecture 23 | The Fourier Transforms and its Applications
Lecture by Professor Brad Osgood for the Electrical Engineering course, The Fourier Transforms and its Applications (EE 261). Professor Osgood lectures on linear systems, focusing on linear time and variance systems. The Fourier transform is a tool for solving physical problems. In thi
From playlist Lecture Collection | The Fourier Transforms and Its Applications
Introduction to Neural Re-Ranking
In this lecture we look at the workflow (including training and evaluation) of neural re-ranking models and some basic neural re-ranking architectures. Slides & transcripts are available at: https://github.com/sebastian-hofstaetter/teaching đź“– Check out Youtube's CC - we added our high qua
From playlist Advanced Information Retrieval 2021 - TU Wien
Involution: Inverting the Inherence of Convolution for Visual Recognition (Research Paper Explained)
#involution #computervision #attention Convolutional Neural Networks (CNNs) have dominated computer vision for almost a decade by applying two fundamental principles: Spatial agnosticism and channel-specific computations. Involution aims to invert these principles and presents a spatial-s
From playlist Papers Explained
17b Machine Learning: Convolutional Neural Networks
Accessible lecture on convolutional neural networks. The Python demonstrations are here: - operators demo - https://git.io/JkqV9 - CNN demo - https://git.io/JksEJ I hope this is helpful, Michael Pyrcz (@GeostatsGuy)
From playlist Machine Learning
Is Memorization Compatible with Learning? by Sasha Rakhlin
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: 04 January 2021 to 08 Januar
From playlist Advances in Applied Probability II (Online)
23. Convolutional Neural Networks
Vanilla neural networks are powerful, but convolutional neural networks are truly revolutionary! Instead of constructing features by hand, a convolutional neural network can extract features on its own! It does this through convolutional layers and then reduces dimensions for faster comput
From playlist Materials Informatics
What can and can't neural networks do: Joel Gibson
Machine Learning for the Working Mathematician: Week Two 3 March 2022 Joel Gibson, What can and can't neural networks do: Universal approximation theorem and convolutional neural networks Seminar series homepage (includes Zoom link): https://sites.google.com/view/mlwm-seminar-2022
From playlist Machine Learning for the Working Mathematician
Convolution via frequency domain multiplication
Is time-domain convolution too slow? (Yes it is.) Learn how to do lightning-fast convolution in the frequency domain. This will also help you understand that wavelet convolution is really just filtering. The video uses files you can download from https://github.com/mikexcohen/ANTS_youtube
From playlist OLD ANTS #3) Time-frequency analysis via Morlet wavelet convolution
Mathematics for Machine Learning - Multivariate Calculus - Full Online Specialism
Welcome to the “Mathematics for Machine Learning: Multivariate Calculus” course, offered by Imperial College London. This video is an online specialisation in Mathematics for Machine Learning (m4ml) hosted by Coursera. For more information on the course and to access the full experience
From playlist Mathematics for Machine Learning - Multivariate Calculus