This document provides an overview of a Deep Learning course, including: - The course code, pre-requisites, title, semester, and class schedule. - The course will include offline classes, labs, assignments, and a group case study. - The course objective is to introduce students to deep neural network architectures, training strategies, challenges, and tools for designing solutions to engineering problems. - The syllabus covers topics like neural networks, convolutional neural networks, recurrent neural networks, and more.

1. 21AI637/21CS716
Deep Learning
M.Tech CSE & AI - II Sem - Elective
AMRITA VISHWA VIDYAPEETHAM
LECTURE 1
D R . S I K H A O K
R E F E R E N C E S : TO WA R D S D ATA S C I E N C E , M A C H I N E L E A R N I N G M A S T E R Y, T E X T B O O K S

2. Welcome To The Course
AMRITA VISHWA VIDYAPEETHAM
 Course code: 21AI637/21CS716
 Pre-Requisite(s): Computational Linear Algebra, Computational Methods for Optimization
 Title: Deep Learning
 Semester: 2
 Batch: M.Tech CSE/AI
Slots:
Monday: Slot 2 (9.35-10.25 AM)
Tuesday : Slot 5 (12.20-1.10 )
Thursday: Slot 3 (10.30am-11.20am )
Thursday: Slot 6-7 (2.00 P M- 4.00 PM)

3. Course Delivery
AMRITA VISHWA VIDYAPEETHAM
 Offline Classes (AB3 D206) ---- Theory
 Lab (AB3 F204) --- 2hrs Per Week
 Assignments ( Problems and programming)
 Case Study(Group)
 Course Repository – AUMS

4. Course Objective
AMRITA VISHWA VIDYAPEETHAM
 To introduce to students, different deep neural network architectures, training
strategies/algorithms, possible challenges, tools and techniques available in designing and
deploying solutions to different practical/Engineering problems.

5. Syllabus
21AI637/21CS716 Deep Learning L-T-P-C: 3-0-2-4
https://kirkpatrickprice.com/blog/classifying-data/
AMRITA VISHWA VIDHYAPEETHAM
Unit 1
Neural Networks basics – Linear Separable Problems and Perceptron – Multi layer neural network sand Back
Propagation, Practical aspects of Deep Learning: Train/ Dev / Test sets, Bias/variance, Vanishing/exploding
gradients, Gradient checking, Hyper Parameter Tuning
Unit 2
Convolutional Neural Networks – Basics and Evolution of Popular CNN architectures –Transfer Learning–
Applications : Object Detection and Localization, Face Recognition, Neural Style Trans-fer Recurrent Neural
Networks–GRU–LSTM–NLP–Word Embeddings–Transfer Learning–Attention Models–Applications: Sentinel
Classification, Speech Recognition, Action Recognition
Unit 3
Restricted Boltzmann Machine– Deep Belief Network– Auto Encoders–Applications: Semi Supervised
classification, Noise Reduction, Non-linear Dimensionality Reduction Goal Oriented Decision Making– Policy and
Target Networks– Deep Quality Network for Reinforcement Learning Introduction to GAN–Encoder/ Decoder,
Generator/ Discriminator architectures Challenges in NN training– Data Augmentation– Hyperparameter
Settings–Transfer Learning–Developing and DeployingMLModels(e.g.,Matlab/TensorFlow/PyTorch)

6. Course Outcome
https://kirkpatrickprice.com/blog/classifying-data/
AMRITA VISHWA VIDHYAPEETHAM
COs Course Outcome
Bloom’s
Taxonomy
Level
CO 1 Be able to design, train, deploy neural networks for solving
different practical/ engineering problems and analyze and report
its efficacy
L4
CO 2 Have a good level of knowledge (Both Conceptual and
Mathematical) on different neural network settings to pursue
Research in this Field.
L3
CO 3 Build skills in using established ML tools/libraries and in
building self-learning skills in the field.
L3

7. CO-PO Mapping
https://kirkpatrickprice.com/blog/classifying-data/
AMRITA VISHWA VIDHYAPEETHAM
PO PO1 PO1 PO2 PO3 PO4 PO5
CO
CO1 3 3 3 3 3 1
CO2 3 3 3 3 1 3
CO3 3 3 3 3 1 3

8. Evaluation Pattern
https://kirkpatrickprice.com/blog/classifying-data/
AMRITA VISHWA VIDHYAPEETHAM
Component Assessments Marks
Internal [70]
Midterm 20
CA(Theory) Quiz(4Quiz*2.5) 10
CA(Lab)
Eval1 -7
40
Project R1-10
Eval2 -8
Project R2-10
Lab assignments-5
External [30] End semester 30
70-30

9. TextBooks
 Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning, MIT Press, Second Edition;
2016: https://www.deeplearningbook.org/
 AdamGibsonandJoshPatterson,”DeepLearning,Apractitioner’sapproach”,O’Reilly,FirstEditi
on,2017.
 FrancoisChollet,”DeepLearningwithPython”,ManningPublicationsCo,FirstEdition,2018.
 ResearchPapersonRelevantTopicsandInternetResources
AMRITA VISHWA VIDHYAPEETHAM
Reference(s)

10. Tools
AMRITA VISHWA VIDHYAPEETHAM
 Google Colab/ Anaconda
 Kaggle Contest
https://forms.office.com/Pages/ResponsePage.aspx?id=o835AF4H5USqC6ujrdZTny9-
f2Cgnx1EoeSmVJuiV_pUNTUzSzJISlBRUjZHMFFZNDJLTFZCSDM0Qy4u

11. What To Do Next??
AMRITA VISHWA VIDHYAPEETHAM
 Brush up BASIC PYTHON
 Explore Google Colab/ Kaggle/ Anaconda

12. Happy learning
Thank you
AMRITA VISHWA VIDHYAPEETHAM