Deep Learning Course for Beginners

Deep Learning Course for Beginners

5/6/2024

Description

This deep learning course is designed to take you from beginner to proficient in deep learning. You will learn the fundamental concepts, architectures, and applications of deep learning in a clear and practical way. So get ready to build, train, and deploy models that can tackle real-world problems across various industries.

Course created by @AyushSinghSh

GitHub: https://github.com/ayush714/core-deep-learning-course/tree/main

⭐️ Contents ⭐️

🎉 Thanks to our Champion and Sponsor supporters:

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👾 Serhiy Kalinets

👾 Justin Hual

👾 Otis Morgan

👾 Oscar Rahnama

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Chapters

Intro
3:07
Getting started
2:00
Vectors
16:44
Operation on vectors
17:01
Matrices
13:10
Operation on Matrices
0:25
Matrix Scalar Multiplication
3:20
Addition of Matrices
3:40
Properties of Matrix addition
3:40
Matrix Multiplication
4:55
Properties of Matrix Multiplication
10:30
Linear Combination Concept
17:48
Span
14:37
Linear Transformation
14:33
Transpose
8:32
Properties of Transpose
5:50
Dot Product
5:30
Geometric Meaning of Dot Product
9:10
Types of Matrices
29:50
Determinant
6:55
Geometric Meaning of Determinant
4:25
Calculating Determinant
7:55
Properties of Determinant
3:45
Rule of Sarus
21:20
Minor
8:07
Cofactor of a Matrix
3:53
Steps to calculate Cofactor of a Matrix
2:35
Adjoint of a Matrix
14:05
Properties of Trace
20:55
System of Equations
24:50
Example
14:35
Determinant
40:05
Single Variable Calculus
5:01
What is Calculus?
8:19
Ideas in Calculus
0:26
Differentiation
7:05
Integration
3:29
Precalculus Functions
21:45
Single Variable Calculus (Trigonometry Review)
1:10
Trigonometry functions
27:00
Unit Circle
12:30
Limit Concept
27:15
Definition of a limit
1:40
Continuity
6:50
Evaluating Limits
16:55
Sandwich Theorem
4:00
Differentiation
24:30
Differentiation as rate of Change
6:55
Differentiation in terms of Limit
12:14
Example
5:03
Important Differentiation Rules
43:18
Rule Chain Rule
24:15
What is Deep Learning
1:00
What is Machine Learning
18:10
Definition of Deep Learning
6:30
Applications
4:12
Introduction to Neural Networks
3:58
Artificial Neural Networks
17:14
The Perceptron
11:26
Linear Neural Network
1:35
Intuition Behind Activation function and Backpropagation Algorithm
2:27:10