Artificial neural networks | Deep learning

Large width limits of neural networks

Artificial neural networks are a class of models used in machine learning, and inspired by biological neural networks. They are the core component of modern deep learning algorithms. Computation in artificial neural networks is usually organized into sequential layers of artificial neurons. The number of neurons in a layer is called the layer width. Theoretical analysis of artificial neural networks sometimes considers the limiting case that layer width becomes large or infinite. This limit enables simple analytic statements to be made about neural network predictions, training dynamics, generalization, and loss surfaces. This wide layer limit is also of practical interest, since finite width neural networks often perform strictly better as layer width is increased. (Wikipedia).

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Computing Limits from a Graph with Infinities

In this video I do an example of computing limits from a graph with infinities.

From playlist Limits

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Algebra - Minimum and Maximum (4 of 4)

Visit http://ilectureonline.com for more math and science lectures! This video is part of a four part lecture in algebra where we'll take a look at problems involved in determining maximums and minimums such as the maximum area a fence of fixed length can enclose in the shape of a square

From playlist ALGEBRA 16 - FINDING MAXIMUM AND MINIMUM VALUES

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Ex: Find Limits of Composite Function Graphically

This video explains how to determine limits of composite function from the graphs of the two functions. Site: http://mathispower4u.com

From playlist Limits at Infinity and Special Limits

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Ex: Limits at Infinity of a Function Involving an Exponential Function

This video provides two examples of how to determine limits at infinity of a function involving a square root. The results are verified graphically. Site: http://mathispower4u.com Blog: http://mathispower4u.wordpress.com

From playlist Limits at Infinity and Special Limits

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Limits of Vector Valued Functions

This video explains how to determine the limit of a vector valued function. http://mathispower4u.yolasite.com/

From playlist Limits

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Limit of (4u^4 + 5)/((u^2 - 2)(2u^2 - 1)) as u approaches infinity

Limit of (4u^4 + 5)/((u^2 - 2)(2u^2 - 1)) as u approaches infinity. This is a calculus problem where we find a limit as u approaches infinity. In this case we have a rational function and the numerator and denominator have the same growth rate, so the limit is the ratio of the leading coef

From playlist Limits at Infinity

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Infinite Limits (Limit Example 10)

Epsilon Definition of a Limit In this video, I illustrate the epsilon-N definition of a limit by doing an example with an infinite limit. More precisely, I prove from scratch that the limit of sqrt(n-2)+3 is infinity Other examples of limits can be seen in the playlist below. Check ou

From playlist Sequences

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How to Compute a One Sided limit as x approaches from the right

In this video I will show you How to Compute a One Sided limit as x approaches from the right.

From playlist One-sided Limits

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A New Physics-Inspired Theory of Deep Learning | Optimal initialization of Neural Nets

A special video about recent exciting developments in mathematical deep learning! 🔥 Make sure to check out the video if you want a quick visual summary over contents of the “The principles of deep learning theory” book https://deeplearningtheory.com/. SPONSOR: Aleph Alpha 👉 https://app.al

From playlist Explained AI/ML in your Coffee Break

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Neural Tangent Kernel theory from High Energy Physics by Junyu Liu

PROGRAM NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (HYBRID) ORGANIZERS: David Berenstein (University of California, Santa Barbara, USA), Simon Catterall (Syracuse University, USA), Masanori Hanada (University of Surrey, UK), Anosh Joseph (II

From playlist NUMSTRING 2022

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Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer (ÎĽTransfer)

👨‍👩‍👧‍👦 Join our Discord community 👨‍👩‍👧‍👦 https://discord.gg/peBrCpheKE In this video I cover "Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer (μTransfer)" paper that makes optimal hyperparameters stable w.r.t. width scaling! ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

From playlist Miscellaneous

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Part 1: Formal Definition of a Limit

This video states the formal definition of a limit and provide an epsilon delta proof that a limit exists. complete Video Library at http://www.mathispower4u.com

From playlist Limits

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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

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Christian Kuehn (7/25/22): Dynamical Systems for Deep Neural Networks

Abstract: In this talk, I am going to explain several approaches to explain the geometry and dynamics of neural networks. First, I will show, why neural networks should always be viewed within the framework of dynamical systems. Then I am going to show how to employ rigorous validated comp

From playlist Applied Geometry for Data Sciences 2022

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Daniel Roberts: "Deep learning as a toy model of the 1/N-expansion and renormalization"

Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Deep learning as a toy model of the 1/N-expansion and renormalization" Daniel Roberts - Diffeo Institute for Pure and Applied Mathematics, UCLA November 20, 2019

From playlist Machine Learning for Physics and the Physics of Learning 2019

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Greg Yang: The unreasonable effectiveness of mathematics in large scale deep learning

23 March 2023 Abstract: Recently, the theory of infinite-width neural networks led to the first technology, muTransfer, for tuning enormous neural networks that are too expensive to train more than once. For example, this allowed us to tune the 6.7 billion parameter version of GPT-3 usin

From playlist SMRI Seminars

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Feature and Lazy Training by Mario Geiger

DISCUSSION MEETING : STATISTICAL PHYSICS OF MACHINE LEARNING ORGANIZERS : Chandan Dasgupta, Abhishek Dhar and Satya Majumdar DATE : 06 January 2020 to 10 January 2020 VENUE : Madhava Lecture Hall, ICTS Bangalore Machine learning techniques, especially “deep learning” using multilayer n

From playlist Statistical Physics of Machine Learning 2020

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Deep Learning Approaches in Inverse Problems (Lecture 1) by Deep Ray

DISCUSSION MEETING WORKSHOP ON INVERSE PROBLEMS AND RELATED TOPICS (ONLINE) ORGANIZERS: Rakesh (University of Delaware, USA) and Venkateswaran P Krishnan (TIFR-CAM, India) DATE: 25 October 2021 to 29 October 2021 VENUE: Online This week-long program will consist of several lectures by

From playlist Workshop on Inverse Problems and Related Topics (Online)

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Limits At Infinity

http://mathispower4u.wordpress.com/

From playlist Limits

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Introduction to Deep Learning | What is Deep Learning | Edureka | Deep Learning Rewind - 1

🔥Edureka Tensorflow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow This Edureka "What is Deep Learning" video will help you to understand the relationship between Deep Learning, Machine Learning and Artificial Intelligence. It will also explain what is Deep learning and

From playlist Edureka Live Classes 2020

Related pages

Artificial neuron | Deep learning | Artificial neural network