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 1 | # Core Deep Learning Course 
 2 | 
 3 | Note - Notes can be found in repository itself and if you've got questions while doing this course - you can join this group for the same [here](https://chat.whatsapp.com/ESdAmUaUbQH7JT9XIZJ5nO)
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 5 | Deep learning is one of the most emerging domains in this era, Learning Deep Learning is worth it! We present ML002 a full deep learning course from scratch covering mathematics basics to some level in deep learning. We saw many students suffering for not getting the right resources to learn for free. 
 6 | 
 7 | We already presented a full-fledged machine learning course ML001 by Antern for absolutely free, covering in-depth machine learning for beginners. Now Antern Presents ML002, a full course on Deep Learning. 
 8 | 
 9 | We also identified some problems that free courses have and feedback from our previous courses and tried to improve our content based on that. So, Let’s get started! 
10 | 
11 | ## Why to Learn Deep Learning? 
12 | 
13 | Whether you’re an aspirant of becoming a data scientist or machine learning engineer, nowadays having a good knowledge of deep learning is a must. It sets you very well and gives you a new way of thinking about “how machines learn?”.  
14 | 
15 | It also sets you a foundation of learning from images and texts, and In this era for data scientists and machine learning engineers or AI Engineers, Deep Learning should be in their skillset. 
16 | 
17 | ## Syllabus: 
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19 | 
20 | ### Module 1: Getting Ready for Deep Learning 
21 | 
22 | * Chapter 1:- Linear Algebra 
23 |   * Basic properties of a matrix and vectors: scalar multiplication, linear transformation, transpose, conjugate, rank, and determinant. 
24 |   * Inner and outer products, matrix multiplication rule and various algorithms, and matrix inverse.
25 |   * Special matrices: square matrix, identity matrix, triangular matrix, the idea about sparse and dense matrix, unit vectors, symmetric matrix, Hermitian, skew-Hermitian and unitary matrices.
26 |   * Vector space, basis, span
27 | 
28 | * Chapter 2:- Calculus  
29 |   * Limits: Introduction, Properties of Limits,  Solving Limits, L-Hopital Rule 
30 |   * Continuity: Introduction, Solving problems, Discontinuities  
31 |   * Differentiability: Introduction, How does it work? Formal Definition, Mean Value Theorem, Minima and Maxima, Gradient Descent, derivative, partial derivative
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33 | 
34 | ### Module 2: Deep Learning Fundamentals 
35 | 
36 | * Chapter 3:- Deep Learning Fundamentals Part - 1  
37 |   * What is Deep Learning? 
38 |   * Evolution of deep learning  
39 |   * Representation learning 
40 |   * History of deep learning  
41 |   * Formal Definition of deep learning 
42 |   * An Overview of Neuron   of a brain 
43 |   * Perceptron and How It relates to Neurons?
44 |   * Logistic Regression as Neural Network  
45 |   * Multi-layer Perceptron
46 |  
47 | * Chapter 4:- Deep Learning Fundamentals Part - 2
48 |   * Perceptron Training  
49 |   * Multi-layer Perceptron Training 
50 |   * BackPropagation Training  
51 |   * Activation Functions and Derivation
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