Lecture Notes || SNS Courseware

Subject Details

Dept : AIML

Sem : 6

Regul : 2019

Faculty : G.L.Karthik

phone : NIL

E-mail : karelectronicscom@gmail.com

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Lecture Notes

UNIT 1:

Introduction to machine learning

Linear models-SVMs

Perceptrons

logistic regression

Intro to Neural Nets: What a shallow network computes

Training a network: loss functions

back propagation

stochastic gradient descent

Neural networks as universal function approximates

UNIT 2:

History of Deep Learning

Back propagation and regularization

batch normalization

VC Dimension

Neural Nets-Deep Vs Shallow Networks

Convolutional Networks

Generative Adversarial Networks (GAN)

Semi-supervised Learning

UNIT 3:

Linear (PCA, LDA)

metric learning - Auto encoders

dimensionality reduction in networks

Introduction to Convnet

Architectures – AlexNet

VGG, Inception, ResNet - Training a Convnet

weights initialization