Deep Learning Course for Beginners
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...
⭐️ 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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