In artificial intelligence, a differentiable neural computer (DNC) is a memory augmented neural network architecture (MANN), which is typically (but not by definition) recurrent in its implementation. The model was published in 2016 by Alex Graves et al. of DeepMind. (Wikipedia).
Differentiability of a function - an example
Free ebook http://tinyurl.com/EngMathYT A simple example of how to determine when a function is differentiable. Such ideas are seen in university mathematics.
From playlist A first course in university mathematics
Deep Learning Lecture 3.3 - Universal Representation Theorem
Deep Learning Lecture - Universal Representation Theorem for neural network functions
From playlist Deep Learning Lecture
Local linearity for a multivariable function
A visual representation of local linearity for a function with a 2d input and a 2d output, in preparation for learning about the Jacobian matrix.
From playlist Multivariable calculus
Multivariable Calculus | Differentiability
We give the definition of differentiability for a multivariable function and provide a few examples. http://www.michael-penn.net https://www.researchgate.net/profile/Michael_Penn5 http://www.randolphcollege.edu/mathematics/
From playlist Multivariable Calculus
How to Wire a Computer Like a Human Brain
The goal of neuromorphic computing is simple: mimic the neural structure of the brain. Meet the current generation of computer chips that's getting closer to reaching this not-so-simple goal. » Subscribe to Seeker! http://bit.ly/subscribeseeker » Watch more Elements! http://bit.ly/Element
From playlist Elements | Seeker
Neuromorphic Computing Is a Big Deal for A.I., But What Is It?
Engineering computers to work like brains could revolutionize technology as we know it. Here’s everything you need to know about neuromorphic computing. Get 20% off http://www.domain.com domain names and web hosting when you use coupon code SEEKER at checkout! Neural Networks: How Do Rob
From playlist Elements | Seeker
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
When is a curve differentiable?
► My Applications of Derivatives course: https://www.kristakingmath.com/applications-of-derivatives-course 0:00 // What is the definition of differentiability? 0:29 // Is a curve differentiable where it’s discontinuous? 1:31 // Differentiability implies continuity 2:12 // Continuity doesn
From playlist Popular Questions
If a function is differentiable then it is continuous
👉 Learn how to determine the differentiability of a function. A function is said to be differentiable if the derivative exists at each point in its domain. To check the differentiability of a function, we first check that the function is continuous at every point in the domain. A function
From playlist Find the Differentiability of a Function
DDPS | Differentiable Programming for Modeling and Control of Dynamical Systems by Jan Drgona
Description: In this talk, we will present a differentiable programming perspective on optimal control of dynamical systems. We introduce differentiable predictive control (DPC) as a model-based policy optimization method that systematically integrates the principles of classical model pre
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
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
Deep Learning of Hierarchical Multiscale Differential Equation Time Steppers
This video by Yuying Liu introduces a new deep learning architecture to accurately and efficiently integrate multiscale differential equations forward in time. This approach is benchmarked on several illustrative dynamical systems. Check out the paper on arXiv: https://arxiv.org/abs/20
From playlist Data-Driven Science and Engineering
Stephan Hoyer: "Improving PDE solvers and PDE-constrained optimization with deep learning and di..."
Machine Learning for Physics and the Physics of Learning 2019 Workshop II: Interpretable Learning in Physical Sciences "Improving PDE solvers and PDE-constrained optimization with deep learning and differentiable programming" Stephan Hoyer - Google Inc. Abstract: Deep learning is differe
From playlist Machine Learning for Physics and the Physics of Learning 2019
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
Deep Learning Lecture 1.3 - Intro Neural Networks
Deep Learning Lecture Introduction: - Neural Networks - Backpropagation
From playlist Deep Learning Lecture
Deep Learning Lecture 3.4 - Backpropagation
Deep Learning Lecture - Backpropagation Algorithm for neural network training.
From playlist Deep Learning Lecture
Ben Recht - Splitting the difference between deep and shallow solutions of inverse problems
Recorded 30 November 2022. Ben Recht of the University of California, Berkeley, presents "Splitting the difference between deep and shallow solutions of inverse problems" at IPAM's Multi-Modal Imaging with Deep Learning and Modeling Workshop. Abstract: The last two dominant paradigms for c
From playlist 2022 Multi-Modal Imaging with Deep Learning and Modeling
Reinhold Schneider: "Solving Backward Stochastic Differential Equation & HJB equations with Tree..."
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop I: Tensor Methods and their Applications in the Physical and Data Sciences "Solving Backward Stochastic Differential Equation and Hamilton Jacobi Bellmann (HJB) equations with Tree Based Tensor Networ
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Mustafa Hajij (07/07/21): Cell Complex Neural Networks
Title: Cell Complex Neural Networks Abstract: Cell complexes are topological spaces constructed from simple blocks called cells. They generalize graphs, simplicial complexes, and polyhedral complexes that form important domains for practical applications. We propose a general, combinator
From playlist AATRN 2021
