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 ⭐️
0:00:00 Intro
0:03:07 Getting started
0:05:07 Vectors
0:21:51 Operation on vectors
0:38:52 Matrices
0:52:02 Operation on Matrices
0:52:27 Matrix Scalar Multiplication
0:55:47 Addition of Matrices
0:59:27 Properties of Matrix addition
1:03:07 Matrix Multiplication
1:08:02 Properties of Matrix Multiplication
1:18:32 Linear Combination Concept
1:36:20 Span
1:50:57 Linear Transformation
2:05:30 Transpose
2:14:02 Properties of Transpose
2:19:52 Dot Product
2:25:22 Geometric Meaning of Dot Product
2:34:32 Types of Matrices
3:04:22 Determinant
3:11:17 Geometric Meaning of Determinant
3:15:42 Calculating Determinant
3:23:37 Properties of Determinant
3:27:22 Rule of Sarus
3:48:42 Minor
3:56:49 Cofactor of a Matrix
4:00:42 Steps to calculate Cofactor of a Matrix
4:03:17 Adjoint of a Matrix
4:18:47 Trace of a Matrix
4:17:22 Properties of Trace
4:38:17 System of Equations
5:03:07 Example
5:17:42 Determinant
5:57:47 Single Variable Calculus
6:02:48 What is Calculus?
6:11:07 Ideas in Calculus
6:11:33 Differentiation
6:18:38 Integration
6:22:07 Precalculus Functions
6:43:52 Single Variable Calculus (Trigonometry Review)
6:45:02 Trigonometry functions
7:12:02 Unit Circle
7:24:32 Limit Concept
7:51:47 Definition of a limit
7:53:27 Continuity
8:00:17 Evaluating Limits
8:17:12 Sandwich Theorem
8:21:12 Differentiation
8:45:42 Differentiation as rate of Change
8:52:37 Differentiation in terms of Limit
9:04:51 Example
9:09:54 Important Differentiation Rules
9:53:12 Rule Chain Rule
10:17:27 What is Deep Learning
10:18:27 What is Machine Learning
10:36:37 Definition of Deep Learning
10:43:07 Applications
10:47:19 Introduction to Neural Networks
10:51:17 Artificial Neural Networks
11:08:31 The Perceptron
11:19:57 Linear Neural Network
11:21:32 Intuition Behind Activation function and Backpropagation Algorithm
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