Deep Learning Lab – CS3807

This repository contains all lab experiments for the Deep Learning Laboratory (CS3807), Shiv Nadar University Chennai, AY 2026–27.

Each experiment is maintained in its own subfolder, with a dedicated README, source code, dataset information, dependency list and execution instructions.

Experiments

#	Experiment	Description	Link
1	Single Layer Perceptron	Binary classification on the Banknote Authentication dataset using a perceptron implemented from scratch	Lab-1-perceptron
2	Multi-Layer Perceptron	Multi-class image classification on the Fashion-MNIST dataset using an MLP with automated hyperparameter optimization	Lab-2-MLP
3	CNN	understand the working principle of Convolutional Neural Networks by implementing convolution, pooling, feature map visualization, and image classification using TensorFlow/Keras.	Lab-3-MLP
3	Transfer Learning	The objective of this experiment are Study the evolution of deep CNN architectures, Compare LeNet-5, AlexNet, VGG16, GoogleNet and ResNet,Understand transfer learning,Fine tune pretrained CNN models,Compare classification performance of different architectures	Lab-4-Transfer-Learning

More experiments will be added here as the semester progresses.

Repository Structure

deep-learning-lab/
├── README.md
├── experiment-1-perceptron/
│   ├── README.md
│   ├── requirements.txt
│   ├── Lab1_perceptron.ipynb
│   └── data_banknote_authentication.txt
├── experiment-2-mlp/
│   ├── README.md
│   ├── requirements.txt
│   └── Lab_2_MLP.ipynb
├── experiment-3-CNN/
│   ├── README.md
│   ├── requirements.txt
│   └── Lab3.ipynb
├── experiment-4-Transfer-Learning/
│   ├── README.md
│   ├── requirements.txt
│   └── Lab_4.ipynb

General Notes

• Each experiment subfolder is self-contained: it can be cloned, its dependencies installed, and its notebook run independently of the others.
• Refer to the README inside each experiment's folder for objective, methodology, results and execution instructions specific to that experiment.