top of page
This site was designed with the
.com
website builder. Create your website today.Start Now

Principles of Deep Learning

This course offers the fundamentals of deep learning technology and its application in real world.

Instructor: Anushka Joshi

Class Area: COER

Email: anushkazinc@gmail.com

ANNOUNCEMENTS

August 22, 2023: Classes will begin

MTE 2: 24 Nov, 2023

​

SYLLABUS (Click on the text to view syllabus)

EVALUATION COMPONENTS

  • Term tests/quizzes (10%)

  • Assignments + Practical (15%)

  • Class participation (5%)

  • Mid-Term Examination (30%)

  • End Term Examination (40%)

COURSE OBJECTIVES, LEARNING OUTCOMES AND PREREQUISITES

  • To understand the basic concepts of deep learning

  • To use python for solving real world deep learning problems involving classification and regression.

​

Prerequisites: NIL

​

LECTURE NOTES

  1. Introduction to Deep Learning: Link

  2. Neural Networks Training: Backpropagation, Bias- Variance Tradeoff, and Regularization: Link
  3. Deep Learning Strategies I and II, Tensorflow: Link
  4. Convolutional Neural Network: Link
  5. Unsupervised Learning:
  6. Reinforcement Learning:
  7. Practical Knowledge of DL:

EXAMINATIONS​

MTE 1: 20 Sep, 2023

MTE 2: 24 Nov, 2023

​

PRACTICE QUIZ

1. Practice Quiz 1: quiz link (click here)

2. Practice Quiz 2: quiz link (click here)

3. Practice Quiz 3: quiz link (click here)

​

ASSIGNMENT

Submission Intructions

1. Assignment 1: link (click here)

2. Assignment 2:

3. Assignment 3: link (click here)

4. Assignment 4: link (click here) Assigned on- 17 Nov 2023, Deadline- 25 Nov 2023

Note: Submit Assignment 4 on this link

MTE Question Papers

1. MTE 1: click here

2. MTE 2: To be conducted...

RECOMMENDED STUDY MATERIAL

​The following will be used as a reference/textbook for this course:

  1. Fei Fei Li et al., "CS231n: Deep Learning for Computer Vision", stanford

  2. Ian Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", MIT Press, 2016

  3. Christopher M. Bishop, "Pattern Recognition and Machine Learning", 2006

  4. Tom Mitchell, "Machine Learning", 1997

  5. Alpaydin, "Ethem Introduction to Machine Learning", 2014

  6. Ian Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", 2016

bottom of page