CS480/680 Lecture 1: Course Introduction

Показать описание

Pascal Poupart

Рекомендации по теме

Комментарии

Thank you very much Sir. Explanation is Excellent!!!. My confusions with function fitting to a data set all cleared. This is the best ML course I found. I continue to get the best out of these lectures. Thank you again Sir

dtakamalakirthidissanayake

Great course. Very good explanations. Thank you for making it available for us.

orhanguengoer

excellent delivery can understand non-native speakers

DaRk_DeViL

7:08 IMO picture information is represented as an _array_ of pixel values, although it is fed into a neural net as a vector (or sequence) of pixel values.

micknamens

glad to see that you are posting your videos for this course. The reinforcement course you taught was exceptional so thank you for this.

JohnDoe-cqic

I appreciate the video! As a 17 year old I am gaining a competitive edge !

amolgill

IMO the usual pronunciation of "algorithm" puts stress on the first and last syllable. When putting stress on the second syllable then it's "Al-Gore-ism". 😀

micknamens

CS480/680 Lecture 1: Course Introduction

CS480/680 Lecture 1: Course Introduction

CS480 Introduction to Machine Learning

CS480/680 Lecture 19: Attention and Transformer Networks

CS 480/680 - Lecture 1A - Perceptron

CS480/680 Lecture 4: Statistical Learning

CS480/680 Lecture 22: Ensemble learning (bagging and boosting)

CS480/680 Lecture 18: Recurrent and recursive neural networks

CS480/680 Lecture 16: Convolutional neural networks

University of Toronto - Introduction to Machine Learning Course - 2019 - Lecture 1

CS 480/680 - Lecture 11b - Deep Networks

CS480/680 Lecture 13: Support vector machines

Lecture 1 - Introduction and Logistics

CS480/680 Lecture 24: Gradient boosting, bagging, decision forests

CS480/680 Lecture 3: Linear Regression

CS480/680 Lecture 2: K-nearest neighbours

CS480/680 Lecture 11: Kernel Methods

CPSC 330 Lecture 1: course intro

Machine Learning (Fall 2019) - Lecture 1

Lecture 1 | The Perceptron - History, Discovery, and Theory

CS480/680 Lecture 6: Model compression for NLP (Ashutosh Adhikari)

CS480/680 Lecture 23: Normalizing flows (Priyank Jaini)

Machine Intelligence - Lecture 1 (methods, history, definitions, Turing Test)

CS885 Lecture 1a: Course Introduction

CS480/680 Lecture 20: Autoencoders