Recursive neural network

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

From playlist Machine Learning Course

Deep Learning Lecture 8.1 - Recurrent Neural Networks

- Introduction to recurrent neural networks (RNNs) - Universal RNNs - Unfolding RNNs - Backpropagation in time

From playlist Deep Learning Lecture

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

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

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

Recurrent Neural Networks

http://www.wolfram.com/training/ Learn about recurrent neural nets and why they are interesting. Find out how you can work with recurrent nets using the neural network framework in the Wolfram Language. See a simple example of integer addition and look at an advanced application of recurr

From playlist Building Blocks for Neural Nets and Automated Machine Learning

Lecture 10 | Recurrent Neural Networks

In Lecture 10 we discuss the use of recurrent neural networks for modeling sequence data. We show how recurrent neural networks can be used for language modeling and image captioning, and how soft spatial attention can be incorporated into image captioning models. We discuss different arch

From playlist Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)

Deep Learning with Tensorflow - Recursive Neural Tensor Networks

Enroll in the course for free at: https://bigdatauniversity.com/courses/deep-learning-tensorflow/ Deep Learning with TensorFlow Introduction The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance,

From playlist Deep Learning with Tensorflow

Lecture 14: Tree Recursive Neural Networks and Constituency Parsing

Lecture 14 looks at compositionality and recursion followed by structure prediction with simple Tree RNN: Parsing. Research highlight ""Deep Reinforcement Learning for Dialogue Generation"" is covered is backpropagation through Structure. Key phrases: RNN, Recursive Neural Networks, MV-RN

From playlist Lecture Collection | Natural Language Processing with Deep Learning (Winter 2017)

Neural networks [10.14] : Natural language processing - recursive network training

From playlist Neural networks class - Université de Sherbrooke

Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3wL2FCD Professor Christopher Manning, Stanford University http://onlinehub.stanford.edu/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Lear

From playlist Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019

Deep Unordered Composition Rivals Syntactic Methods for Text Classification

Full paper at https://www.cs.colorado.edu/~jbg/docs/2015_acl_dan.pdf

From playlist Research Talks

Neural networks [10.13] : Natural language processing - tree inference

From playlist Neural networks class - Université de Sherbrooke

Neural networks [10.11] : Natural language processing - recursive network

From playlist Neural networks class - Université de Sherbrooke

Deep Learning Lecture 8.2 - Recurrent Neural Networks 2

- Simple RNN Example - Teacher forcing - Deep RNNs

From playlist Deep Learning Lecture

Recurrent Neural Nets: Joel Gibson

Machine Learning for the Working Mathematician: Week Four 17 March 2022 Joel Gibson, Recurrent Neural Nets Part Two (Georg Gottwald): https://youtu.be/1MA5OTCbnqM Seminar series homepage (includes Zoom link): https://sites.google.com/view/mlwm-seminar-2022

From playlist Machine Learning for the Working Mathematician

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

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