3 Months Uncool, Comprehensive and Rigorous Deep Learning Course

with focus on mathematics and implementation

Register - https://in.explara.com/e/deep-learning--3-month-course

 

Reference Books  - 

  1. Machine Learning - Tom Mitchell

  2. Neural Networks and Learning Machines - Simon Haykin

  3. Deep Learning - Ian Goodfellow et.al

  4. Pattern Recognition and Machine Learning - Bishop

Framework - 

  1. PyTorch

 

Course Aim - Thorough understanding of foundations of Deep Learning.

 

Prerequisites - 

  1. You should have a laptop on which google colab can be run

  2. Good grasp on 12th level mathematics

Fee - Rs 10000 (One time payment), Rs 5000 (monthly for 3 months)

Mode of delivery - Recorded Lectures delivered during the week + Live special sessions on weekends(Only for those who complete assignments).

Refund - full refund after the course starts.

Syllabus -
 

Module 0 - Introduction to Python

 

Module 1 - Necessary Mathematics

 

  1. Probability Theory

  2. Information Theory

  3. Probability Distributions

  4. Linear Algebra

    1. Matrices

    2. Determinants

    3. Vector Algebra

 

Module 2 - Introduction to Neural Networks - 

  1. Universal Approximation Theorem

  2. Introduction to PyTorch

  3. Neural Networks

    1. Back-propagation in detail

    2. Paper Review - Theoretical framework for back-propagation

    3. Implementing Neural Network in python using numpy.

    4. Implementing Simple Neural Network in PyTorch.

  4. Regularisation

    1. Dropouts

    2. Paper Review - Dropouts

    3. Batch Normalisation

    4. Paper Review - Understanding the Disharmony between Dropout and Batch Normalisation by Variance Shift

  5. Optimisations

Module 3 - Convolution Neural Networks

Theory and Implementation

 

Module 4 - Recurrent Neural Networks

Theory and Implementation

 

Module 5 - Advanced Computer Vision Neural Networks

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