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Essential Matrix Algebra for Neural Networks

Offered By: StatQuest with Josh Starmer via YouTube

Tags

Linear Algebra Courses Machine Learning Courses Deep Learning Courses Neural Networks Courses PyTorch Courses ChatGPT Courses Linear Transformations Courses Matrix Operations Courses Attention Mechanisms Courses

Course Description

Overview

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Learn essential matrix algebra concepts crucial for understanding and implementing neural networks in this 30-minute video tutorial. Explore linear transformations, matrix notation, multiplication, and transposition. Discover how to represent neural networks using matrix equations, interpret PyTorch documentation, and decipher error messages. Gain insights into the fundamental concepts behind ChatGPT by examining the matrix equation for Attention. Perfect for those looking to enhance their coding skills and comprehend cutting-edge research in the field of neural networks.

Syllabus

Awesome song and introduction
Introduction to linear transformations
Linear transformations in matrix notation
Matrix multiplication
Matrix multiplication consolidates a sequence of linear transformations
Transposing a matrix
Matrix notation and equations
Using matrix equations to describe a neural network
nn.Linear documentation explained
1-D vs 2-D error messages explained
The matrix equation for Attention explained


Taught by

StatQuest with Josh Starmer

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