Neural Networks 1 Neural Units
From playlist Week 5: Neural Networks
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
Neural networks have been the come-back kids of machine learning. They were invented in the 80s, but surged back into the limelight in the late 90s to become a powerful tool at the heart of deep learning. This video describes what neural networks are, how they work in materials informatics
From playlist Materials Informatics
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
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
This lecture discusses some key limitations of neural networks and suggests avenues of ongoing development. 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 š„Enroll for Free Machine Learning
From playlist Machine Learning Algorithms [2022 Updated]
Discovering Symbolic Models from Deep Learning with Inductive Biases (Paper Explained)
Neural networks are very good at predicting systems' numerical outputs, but not very good at deriving the discrete symbolic equations that govern many physical systems. This paper combines Graph Networks with symbolic regression and shows that the strong inductive biases of these models ca
From playlist Papers Explained
DDPS | "When and why physics-informed neural networks fail to train" by Paris Perdikaris
Physics-informed neural networks (PINNs) have lately received great attention thanks to their flexibility in tackling a wide range of forward and inverse problems involving partial differential equations. However, despite their noticeable empirical success, little is known about how such c
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Paris Perdikaris: "Overcoming gradient pathologies in constrained neural networks"
Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Overcoming gradient pathologies in constrained neural networks" Paris Perdikaris - University of Penns
From playlist Machine Learning for Physics and the Physics of Learning 2019
Deep Learning and Computations of PDEs by Siddhartha Mishra
COLLOQUIUM DEEP LEARNING AND COMPUTATIONS OF PDES SPEAKER: Siddhartha Mishra (Professor of Applied Mathematics, ETH ZĆ¼rich, Switzerland) DATE & TIME: Mon, 27 June 2022, 15:30 to 17:00 VENUE: Online Colloquium ABSTRACT Partial Differential Equations (PDEs) are ubiquitous in the scien
From playlist ICTS Colloquia
DDPS | The problem with deep learning for physics (and how to fix it) by Miles Cranmer
Description: I will present a review of how deep learning is used in physics, and how this use is often misguided. I will introduce the term āscientific debt,ā and argue that, though deep learning can quickly solve a complex problem, its success does not come for free. Because most learnin
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
DDPS | A mathematical understanding of modern Machine Learning: theory, algorithms and applications
In this talk from July 15, 2021, Brown University assistant professor Yeonjong Shin discusses the development of robust and reliable machine learning algorithms based on insights gained from the mathematical analysis. Description: Modern machine learning (ML) has achieved unprecedented em
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Intro to Neural Networks : Data Science Concepts
A gentle intro to neural networks. Perceptron Video : https://www.youtube.com/watch?v=4Gac5I64LM4 Logistic Regression Video : https://www.youtube.com/watch?v=9zw76PT3tzs My Patreon : https://www.patreon.com/user?u=49277905
From playlist Data Science Concepts
DIRECT 2021 12 Scientific Machine Learning
DIRECT Consortium at The University of Texas at Austin, working on novel methods and workflows in spatial, subsurface data analytics, geostatistics and machine learning. This is Applications of Scientific Machine Learning for Petroleum Engineering. Join the consortium for access to all
From playlist DIRECT Consortium, The University of Texas at Austin
A Hands-on Introduction to Physics-informed Machine Learning
2021.05.26 Ilias Bilionis, Atharva Hans, Purdue University Table of Contents below. This video is part of NCN's Hands-on Data Science and Machine Learning Training Series which can be found at: https://nanohub.org/groups/ml/handsontraining Can you make a neural network satisfy a physical
From playlist ML & Deep Learning
Neural Networks and Deep Learning
This lecture explores the recent explosion of interest in neural networks and deep learning in the context of 1) vast and increasing data sets, and 2) rapidly improving computational hardware, which have enabled the training of deep neural networks. Book website: http://databookuw.com/
From playlist Intro to Data Science