Deep Learning Course for Beginners

This heavy learning people is designed to return you from beginner to proficient successful heavy learning. You will study nan basal concepts, architectures, and applications of heavy learning successful a clear and applicable way. So get fresh to build, train, and deploy models that tin tackle real-world problems crossed 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 connected vectors 0:38:52 Matrices 0:52:02 Operation connected 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 cipher 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 successful 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 arsenic complaint of Change 8:52:37 Differentiation successful position 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 usability and Backpropagation Algorithm 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to codification for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles connected programming: https://freecodecamp.org/news