Core Deep Learning Course

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

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.

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.

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!

Why to Learn Deep Learning?

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?”.

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.

Syllabus:

Module 1: Getting Ready for Deep Learning

• Chapter 1:- Linear Algebra

  • Basic properties of a matrix and vectors: scalar multiplication, linear transformation, transpose, conjugate, rank, and determinant.
  • Inner and outer products, matrix multiplication rule and various algorithms, and matrix inverse.
  • 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.
  • Vector space, basis, span

• Chapter 2:- Calculus

  • Limits: Introduction, Properties of Limits, Solving Limits, L-Hopital Rule
  • Continuity: Introduction, Solving problems, Discontinuities
  • Differentiability: Introduction, How does it work? Formal Definition, Mean Value Theorem, Minima and Maxima, Gradient Descent, derivative, partial derivative

Module 2: Deep Learning Fundamentals

• Chapter 3:- Deep Learning Fundamentals Part - 1

  • What is Deep Learning?
  • Evolution of deep learning
  • Representation learning
  • History of deep learning
  • Formal Definition of deep learning
  • An Overview of Neuron of a brain
  • Perceptron and How It relates to Neurons?
  • Logistic Regression as Neural Network
  • Multi-layer Perceptron

• Chapter 4:- Deep Learning Fundamentals Part - 2

  • Perceptron Training
  • Multi-layer Perceptron Training
  • BackPropagation Training
  • Activation Functions and Derivation