kartz3011 / COT5615

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

COT5615

Unit 1: Introduction

Motivating examples. Using Julia, Jupyter, Markdown and basic latex formulas. No prior reading is needed. Intro to Julia

Unit 2: Vectors (week 2)

From [VMLS]: 1.1 Vectors, 1.2 Vector addition, 1.3 Scalar-vector Multiplication, 1.4 Inner Product, 1.5 Complexity of vector computations, 2.1 Linear Functions, 2.2 Taylor approximation, C.1.2 Scalar-valued function of a vector, C.1.3 Vector-valued function of a vector.
From [3B1B]:
Vectors, what even are they? | Essence of linear algebra, chapter 1

Unit 3: Using Vectors (week 3)

From [VMLS]: 3.1 Norm, 3.2 Distance, 3.3 Standard deviation, 3.4 Angle, 4.1 Clustering, 4.2 A clustering objective, 4.3 The k-means Algorithm.
From [3B1B]:
But what is a Neural Network? | Deep learning, chapter 1

Gradient descent, how neural networks learn | Deep learning, chapter 2

What is backpropagation really doing? | Deep learning, chapter 3

Backpropagation calculus | Deep learning, chapter 4

Install Julia

Install Julia

About

License:MIT License


Languages

Language:Julia 97.9%Language:TeX 2.1%