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INT422 — Deep Learning

Course Overview

INT422 3 Credits L:2 T:0 P:2 Minor Machine Learning
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This course develops skills in building and optimizing deep learning models using TensorFlow and Keras. It covers NVIDIA DGX Station A100, convolutional and recurrent neural networks, autoencoders, generative adversarial networks, and model deployment using Docker and Streamlit.

Unit 1

Building Models with TensorFlow

Unit 2

Building Models with Keras

Unit 3

Classifying Images with Deep Convolutional Neural Networks

Unit 4

Autoencoders and Pre-trained CNN

Unit 5

Modeling Sequential Data Using Recurrent Neural Networks

Unit 6

Generative Adversarial Networks

Continuous Assessment

3 components

Best 2 of 3 CAs will be considered for evaluation.
Project 50%

Each student will be assigned a project topic and evaluated based on the innovation of the solution, presentation skills, and report writing.

Week 3 / 11

Rubric
To assess the skills of the individual in the learned concepts of deep learning, TensorFlow, Keras, CNN, and RNN.

Test 1 50%

This task will contain topics from unit 1 and unit 2.

Week 4 / 5

Rubric
To check the knowledge of students on learned topics.

Test 2 50%

Subjective test covering syllabus from CA1 and CA2.

Week 11 / 12

Rubric
To test the subject knowledge of the students and provide an opportunity to improve their CA performance in case of low marks or any missed CA.

Exams & Practice

Mid Term Examination

Mid-semester comprehensive evaluation

20%

End Term Examination

Final semester comprehensive evaluation

50%

Type: Examination

INT422 - FAQs

How many units are in INT422?

INT422 has 6 units. Each unit includes detailed notes and MCQ practice questions.

What exam resources are available for INT422?

Unit-wise notes and MCQ practice are available. Exam resources coming soon.

How to prepare for INT422 exams?

Study each unit's notes thoroughly, practice MCQs to test understanding, and attempt mock tests before exams. Focus on important topics and previous year questions.