Linear classifier

Linear classifiers (1): Basics

Definitions; decision boundary; separability; using nonlinear features

From playlist cs273a

what is linear and non linear in machine learning, deep learning

what is linear and non linear in machine learning and deep learning? you will have clear understanding after watching this video. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6

From playlist Machine Learning

4.4 Linear Classifier

From playlist Big Data Analytics by Dr. Emmanuel Müller

Identifying Linear Functions

Define linear functions. Use function notation to evaluate linear functions. Learn to identify linear function from data, graphs, and equations.

From playlist Algebra 1

Training Your Logistic Classifier

This video is part of the Udacity course "Deep Learning". Watch the full course at https://www.udacity.com/course/ud730

From playlist Deep Learning | Udacity

Zeros of Linear Functions

From playlist 4. Attributes of Linear Functions and Equations

Definition of Linear Combination and How to Show a Vector is a Linear Combination of Other Vectors

Definition of Linear Combination and How to Show a Vector is a Linear Combination of Other Vectors More Linear Algebra! This starts with the definition of a Linear Combination and then we show a Vector in R^3 is a linear combination of other vectors in R^3. Solid example. I hope this help

From playlist Linear Algebra

Linear classifiers (2): Learning parameters

Perceptron algorithm, logistic regression, and surrogate loss functions

From playlist cs273a

Linear regression

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

Spotlight Talks - Amir Asadi, Dimitris Kalimeris

Workshop on Theory of Deep Learning: Where next? Topic: Spotlight Talks Speaker: Amir Asadi, Dimitris Kalimeris Date: October 15, 2019 For more video please visit http://video.ias.edu

From playlist Mathematics

OpenAI CLIP: ConnectingText and Images (Paper Explained)

#ai #openai #technology Paper Title: Learning Transferable Visual Models From Natural Language Supervision CLIP trains on 400 million images scraped from the web, along with text descriptions to learn a model that can connect the two modalities. The core idea is a contrastive objective co

From playlist Papers Explained

A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)

Does self-supervision really need a lot of data? How low can you go? This paper shows that a single image is enough to learn the lower layers of a deep neural network. Interestingly, more data does not appear to help as long as enough data augmentation is applied. OUTLINE: 0:00 - Overview

From playlist Papers Explained

CSE 519 -- Lecture 25, Fall 2020

From playlist CSE 519 -- Fall 2020

CS231n Lecture 2 - Data driven approach, kNN, Linear Classification 1

Image classification and the data-driven approach k-nearest neighbor Linear classification I

From playlist CS231N - Convolutional Neural Networks

Felix Klein Lectures 2020: Quiver moduli and applications, Markus Reineke (Bochum), Lecture 1

Quiver moduli spaces are algebraic varieties encoding the continuous parameters of linear algebra type classification problems. In recent years their topological and geometric properties have been explored, and applications to, among others, Donaldson-Thomas and Gromov-Witten theory have

From playlist Felix Klein Lectures 2020: Quiver moduli and applications, Markus Reineke (Bochum)

Lecture 23 - Machine Learning

This is Lecture 23 of the CSE519 (Data Science) course taught by Professor Steven Skiena [http://www.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: http://www.cs.stonybrook.edu/~skiena/519 More information may be found here: http://www

From playlist CSE519 - Data Science Fall 2016

Foundations for Learning in the Age of Big Data III - Maria Florina Balcan

2022 Program for Women and Mathematics: The Mathematics of Machine Learning Topic: Foundations for Learning in the Age of Big Data III Speaker: Maria Florina Balcan Affiliation: Carnegie Mellon University Date: May 26, 2022 In computer vision, generalization of neural representations is

From playlist Mathematics

Applied Machine Learning 2019 - Lecture 07 - Linear Models for Classifications, SVMs

Logistic Regression, linear SVMs, the kernel trick One-vs-Rest and One-vs-One multi-class strategies. Class website with slides and more materials: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/

From playlist Applied Machine Learning - Spring 2019

Determining if a vector is a linear combination of other vectors

Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Determining if a vector is a linear combination of other vectors

From playlist Linear Algebra

Statistical Learning: 9.4 Example and Comparison with Logistic Regression

Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning

From playlist Statistical Learning