This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
What is Neural Network in Machine Learning | Neural Network Explained | Neural Network | Simplilearn
This video by Simplilearn is based on Neural Networks in Machine Learning. This Neural Network in Machine Learning Tutorial will cover the fundamentals of Neural Networks along with theoretical and practical demonstrations for a better learning experience đ„Enroll for Free Machine Learning
From playlist Machine Learning Algorithms [2022 Updated]
Star Network - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
https://www.patreon.com/edmundsj If you want to see more of these videos, or would like to say thanks for this one, the best way you can do that is by becoming a patron - see the link above :). And a huge thank you to all my existing patrons - you make these videos possible. What is a wav
From playlist Waveguides
In this video, I present some applications of artificial neural networks and describe how such networks are typically structured. My hope is to create another video (soon) in which I describe how neural networks are actually trained from data.
From playlist Machine Learning
Neural Networks 1 Neural Units
From playlist Week 5: Neural Networks
Stanford Seminar - Computing with Physical Systems
Peter McMahon, Cornell University June 1, 2022 With conventional digital computing technology reaching its limits, there has been a renaissance in analog computing across a wide range of physical substrates. In this talk I will introduce the concept of Physical Neural Networks [1] and des
From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series
Neural Network Architectures & Deep Learning
This video describes the variety of neural network architectures available to solve various problems in science ad engineering. Examples include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Book website: http://databookuw.com/ Steve Brunton
From playlist Data Science
CS231n Lecture 14 - Videos and Unsupervised Learning
ConvNets for videos Unsupervised learning
From playlist CS231N - Convolutional Neural Networks
Ulugbek Kamilov: Signal processing for nonlinear diffractive imaging
Abstract: Can modern signal processing be used to overcome the diffraction limit? The classical diffraction limit states that the resolution of a linear imaging system is fundamentally limited by one half of the wavelength of light. This implies that conventional light microscopes cannot d
From playlist Probability and Statistics
Kevin Kelly - Machine Learning Enhanced Compressive Hyperspectral Imaging - IPAM at UCLA
Recorded 02 December 2022. Kevin Kelly of Rice University Electrical and Computer Engineering presents "Machine Learning Enhanced Compressive Hyperspectral Imaging" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: This talk reviews approaches to combine com
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
Demetri Psaltis - Machine Learning for 3D Optical Imaging - IPAM at UCLA
Recorded 13 October 2022. Demetri Psaltis of the Ăcole Polytechnique FĂ©dĂ©rale de Lausanne (EPFL) presents "Machine Learning for 3D Optical Imaging" at IPAM's Diffractive Imaging with Phase Retrieval Workshop. Abstract: In optical diffraction tomography (ODT), the 3D shape of an object is r
From playlist 2022 Diffractive Imaging with Phase Retrieval - - Computational Microscopy
George Barbastathis - On the use of machine learning for computational imaging - IPAM at UCLA
Recorded 11 October 2022. George Barbastathis of the Massachusetts Institute of Technology presents "On the use of machine learning for computational imaging" at IPAM's Diffractive Imaging with Phase Retrieval Workshop. Abstract: Computational inverse problems always rely on regularization
From playlist 2022 Diffractive Imaging with Phase Retrieval - - Computational Microscopy
https://www.patreon.com/edmundsj If you want to see more of these videos, or would like to say thanks for this one, the best way you can do that is by becoming a patron - see the link above :). And a huge thank you to all my existing patrons - you make these videos possible. In this video
From playlist Optoelectronic and Photonic Devices
All-optical machine learning using diffractive deep neural networks | TDLS
Toronto Deep Learning Series, 10 September 2018 For slides and more information, visit https://tdls.a-i.science/events/2018-09-10/ Paper Review: http://innovate.ee.ucla.edu/wp-content/uploads/2018/07/2018-optical-ml-neural-network.pdf Speaker: https://www.linkedin.com/in/russell-pollari
From playlist Machine Learning for Scientific Discovery
Ivan Pedro Lobato Hoyos - Application of machine learning to electron microscopy data - IPAM at UCLA
Recorded 25 October 2022. Ivan Pedro Lobato Hoyos of the University of Antwerp presents "Application of machine learning to electron microscopy data" at IPAM's Mathematical Advances for Multi-Dimensional Microscopy Workshop. Abstract: Recent advances in the data acquisition process in the
From playlist 2022 Mathematical Advances for Multi-Dimensional Microscopy
Ulugbek Kamilov: "Computational Imaging: Reconciling Models and Learning"
Deep Learning and Medical Applications 2020 "Computational Imaging: Reconciling Models and Learning" Ulugbek Kamilov, Washington University in St. Louis Abstract: There is a growing need in biological, medical, and materials imaging research to recover information lost during data acquis
From playlist Deep Learning and Medical Applications 2020
Introduction to Optoelectronics and Photonics
https://www.patreon.com/edmundsj If you want to see more of these videos, or would like to say thanks for this one, the best way you can do that is by becoming a patron - see the link above :). And a huge thank you to all my existing patrons - you make these videos possible. This is part
From playlist Optoelectronic and Photonic Devices
DDPS | Machine Learning for materials and chemical dynamics by Sergei Tretiak
Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. Generally, ML provides a surrogate model trained on the dataset of some reference data. This model establishes a relationship between structure and underlying chemical properties, guidin
From playlist Data-driven Physical Simulations (DDPS) Seminar Series