Course Modules

Explore the complete curriculum with hands-on exercises and real-world applications

Course Syllabus (PDF)
1

Introduction to Deep Learning

Foundations, history, and key concepts

Neural Networks Basics
Perceptrons
History of AI
2

Backpropagation

The algorithm that powers deep learning

Backpropagation
Gradient Descent
Activation Functions
3

Optimization Foundations & Ablation Methodology

Optimization algorithms and experimental design

Optimizers
Learning Rate Scheduling
Regularization
4

Convolutional Neural Networks

CNNs for computer vision applications

Convolution
Pooling
CNN Architectures
5

Advanced CNN Architectures

ResNet, DenseNet, and modern architectures

ResNet
DenseNet
EfficientNet
6

Encoder Decoder Architectures

encoder–decoder design

Seq2Seq
Encoder–Decoder
U-Net
7

Recurrent Neural Networks

RNNs, LSTMs, and sequence modeling

RNN Basics
LSTM
GRU
8

Under construction

9

Under construction

10

Under construction

11

Under construction

12

Under construction