Neural Network Fundamentals (Part3): Regression
From http://www.heatonresearch.com. In this part we will see how to represent data to a neural network with regression. We will see how this is different than classification.
From playlist Neural Networks by Jeff Heaton
We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity promoting techniques to select the nonlinear and partial derivative
From playlist Research Abstracts from Brunton Lab
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
Neural Network Fundamentals (Part 2): Classification and Regression
From http://www.heatonresearch.com. In this part we will see how to use classification and regression to represent data to a neural network. This part will focus on classification.
From playlist Neural Networks by Jeff Heaton
(ML 19.10) GP regression - the key step
The key step in deriving the posterior predictive distribution for a Gaussian process regression model just involves the basic properties of multivariate Gaussians.
From playlist Machine Learning
Linear regression ANOVA ANCOVA Logistic Regression
In this video tutorial you will learn about the fundamentals of linear modeling: linear regression, analysis of variance, analysis of covariance, and logistic regression. I work through the results of these tests on the white board, so no code and no complicated equations. Linear regressi
From playlist Statistics
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 “scientific debt,” 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
Interpretable Deep Learning for New Physics Discovery
In this video, Miles Cranmer discusses a method for converting a neural network into an analytic equation using a particular set of inductive biases. The technique relies on a sparsification of latent spaces in a deep neural network, followed by symbolic regression. In their paper, they de
From playlist Data-Driven Dynamical Systems with Machine Learning
If you’re like me, you don’t really need to train self-driving car algorithms or make a cat-image-detectors. Instead, you're likely dealing with practical problems and normal looking data. The focus of this series is to help the practitioner develop intuition about when and how to use D
From playlist Python Keras — Deep Learning Building Blocks
From playlist Machine Learning Course
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3kzqrg1 Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial
From playlist Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019
An Introduction to Linear Regression Analysis
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Lon
From playlist Linear Regression.
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
What do neural networks learn?
Part of the End-to-End Machine Learning Course 193, How Neural Networks Work at http://e2eml.school/193 Blog post: https://brohrer.github.io/what_nns_learn.html We open the black box of neural networks and take a closer look at what they can actually learn. This is exploration and exposit
From playlist E2EML 193. How Neural Networks Work
Deep Learning Lecture 9: Neural networks and modular design in Torch
Slides available at: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ Course taught in 2015 at the University of Oxford by Nando de Freitas with great help from Brendan Shillingford.
From playlist Deep learning at Oxford 2015
Lecture 08: Function Approximation with Supervised Learning
Eighth lecture video on the course "Reinforcement Learning" at Paderborn University during the summer term 2020. Source files are available here: https://github.com/upb-lea/reinforcement_learning_course_materials
From playlist Reinforcement Learning Course: Lectures (Summer 2020)
From playlist Coursera Regression V2
An introduction to Regression Analysis
Regression Analysis, R squared, statistics class, GCSE Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos Playlist on Linear Regression http://www.youtube.com/playlist?list=PLF596A4043DBEAE9C Using SPSS for Multiple Linear Regression http://www.youtube.com/playlist?li
From playlist Linear Regression.
A Conversational Agent for Neural Networks: Construction, Training and Utilization
To learn more about Wolfram Technology Conference, please visit: https://www.wolfram.com/events/technology-conference/ Speaker: Anton Antonov Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, and
From playlist Wolfram Technology Conference 2018