Rectifier (neural networks)

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

Practical 4.0 – RNN, vectors and sequences

Recurrent Neural Networks – Vectors and sequences Full project: https://github.com/Atcold/torch-Video-Tutorials Links to the paper Vinyals et al. (2016) https://arxiv.org/abs/1609.06647 Zaremba & Sutskever (2015) https://arxiv.org/abs/1410.4615 Cho et al. (2014) https://arxiv.org/abs/1406

From playlist Deep-Learning-Course

Recurrent Neural Networks : Data Science Concepts

My Patreon : https://www.patreon.com/user?u=49277905 Neural Networks Intro : https://www.youtube.com/watch?v=xx1hS1EQLNw Backpropagation : https://www.youtube.com/watch?v=kbGu60QBx2o 0:00 Intro 3:30 How RNNs Work 18:15 Applications 21:06 Drawbacks

From playlist Time Series Analysis

Neural Networks 1 Neural Units

From playlist Week 5: Neural Networks

Neural Network Overview

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

Recursive Neural Tensor Nets - Ep. 11 (Deep Learning SIMPLIFIED)

Certain patterns are innately hierarchical, like the underlying parse tree of a natural language sentence. A Recursive Neural Tensor Network (RNTN) is a powerful tool for deciphering and labelling these types of patterns. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearning

From playlist Deep Learning SIMPLIFIED

Recurrent Neural Networks (RNN) - Deep Learning with Neural Networks and TensorFlow 10

In this Deep Learning with TensorFlow tutorial, we cover the basics of the Recurrent Neural Network, along with the LSTM (Long Short Term Memory) cell, which is a very common RNN cell used. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogrammin

From playlist Machine Learning with Python

Deep Learning Lecture 3.2 - Neural Network Basics

Deep Learning Lecture - Neural Network Intro: - Artifical neurons - Perceptron - Multiplayer perceptron / dense neural networks - Activation functions

From playlist Deep Learning Lecture

Serhiy Yanchuk - Adaptive dynamical networks: from multiclusters to recurrent synchronization

Recorded 02 September 2022. Serhiy Yanchuk of Humboldt-Universität presents "Adaptive dynamical networks: from multiclusters to recurrent synchronization" at IPAM's Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond. Abstract: Adaptive dynamical networks is

From playlist 2022 Reconstructing Network Dynamics from Data: Applications to Neuroscience and Beyond

How Deep Neural Networks Work

Part of the End-to-End Machine Learning School Course 193, How Neural Networks Work at https://e2eml.school/193 Visit the blog: https://brohrer.github.io/how_neural_networks_work.html Get the slides: https://docs.google.com/presentation/d/1AAEFCgC0Ja7QEl3-wmuvIizbvaE-aQRksc7-W8LR2GY/edit

From playlist E2EML 193. How Neural Networks Work

CNN Tutorial for Beginners | Convolutional Neural Network | CNN Tutorial Python | Simplilearn

🔥 Professional Certificate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=10March2023CNNTutorialforBeginners&utm_medium=DescriptionFirstFold&utm_source=youtube 🔥 Artificial Intelligence Engineer Master's

From playlist 🔥Machine Learning | Machine Learning Tutorial For Beginners | Machine Learning Projects | Simplilearn | Updated Machine Learning Playlist 2023

Recursively Defined Sets - An Intro

Recursively defined sets are an important concept in mathematics, computer science, and other fields because they provide a framework for defining complex objects or structures in a simple, iterative way. By starting with a few basic objects and applying a set of rules repeatedly, we can g

From playlist All Things Recursive - with Math and CS Perspective

How convolutional neural networks work, in depth

Part of the End-to-End Machine Learning School Course 193, How Neural Networks Work at https://e2eml.school/193 slides: https://docs.google.com/presentation/d/1R-DnrghbU36jO8X4scbrrlx6gFyJHgSL3bD274sutng/edit?usp=sharing machine learning blog: https://brohrer.github.io/blog.html

From playlist E2EML 193. How Neural Networks Work

How Deep Neural Networks Work - Full Course for Beginners

Even if you are completely new to neural networks, this course will get you comfortable with the concepts and math behind them. Neural networks are at the core of what we are calling Artificial Intelligence today. They can seem impenetrable, even mystical, if you are trying to understand

From playlist Machine Learning

The Function That Changed Everything

This is a story about the unreasonable effectiveness of the function that made deep learning possible. Citations: https://gist.github.com/svpino/8c34ecb612f9f66c13f7542a9e5043cc 🔔 Subscribe for more stories: https://www.youtube.com/@underfitted?sub_confirmation=1 📚 My 3 favorite Machine

From playlist Stories

Recurrent Neural Networks (RNN) - Deep Learning w/ Python, TensorFlow & Keras p.7

In this part we're going to be covering recurrent neural networks. The idea of a recurrent neural network is that sequences and order matters. For many operations, this definitely does. Text tutorials and sample code: https://pythonprogramming.net/recurrent-neural-network-deep-learning-py

From playlist Deep Learning basics with Python, TensorFlow and Keras

SN Partial Differential Equations and Applications Webinar - Arnulf Jentzen

Join Arnulf Jentzen of University of Münster as he proves that suitable deep neural network approximations do indeed overcome the curse of dimensionality in the case of a general class of semilinear parabolic PDEs and thereby proves, for the first time, that a general semilinear parabolic

From playlist Talks of Mathematics Münster's reseachers

Kaggle Reading Group: Neural Networks and Neural Language Models | Kaggle

Join Kaggle Data Scientist Rachael as she reads through an NLP paper! Today's paper is the chapter "Neural Networks and Neural Language Models" from "Speech and Language Processing" by Daniel Jurafsky & James H. Martin. This chapter is new to the currently-in-progress edition of the book,

From playlist Kaggle Reading Group | Kaggle

CNN Tutorial for Beginners | Convolutional Neural Network | CNN Tutorial Python | Simplilearn

🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=CNNTutorialforBeginners-IPdfor9ROf4&utm_medium=DescriptionFirstFold&utm_source=youtube 🔥Professional Certificate Program In AI And M

From playlist 🔥Machine Learning | Machine Learning Tutorial For Beginners | Machine Learning Projects | Simplilearn | Updated Machine Learning Playlist 2023

Multilayer Neural Networks - Part 1: Introduction

This video is about Multilayer Neural Networks - Part 1: Introduction Abstract: This is a series of video about multi-layer neural networks, which will walk through the introduction, the architecture of feedforward fully-connected neural network and its working principle, the working prin

From playlist Neural Networks