- Name: Pritish Dutta
- Roll Number: 102303473
- Sub Group: 3C34
This repository contains the implementation of Deep Learning laboratory experiments from Lab 1 to Lab 7. Each lab focuses on different concepts and practical implementations related to deep learning and neural networks.
The experiments are implemented using Python and commonly used deep learning libraries.
Introduction to deep learning concepts and basic Python/Numpy operations used in neural networks.
Implementation of fundamental neural network operations such as activation functions and loss functions.
Building a simple neural network model and understanding forward and backward propagation.
Training neural networks using optimization techniques and gradient descent.
Implementation of deep learning models using frameworks such as TensorFlow or PyTorch.
Working with convolutional neural networks (CNNs) for image-based tasks.
Advanced deep learning concepts and model evaluation techniques.
- Python
- NumPy
- TensorFlow / PyTorch
- Matplotlib
- Jupyter Notebook
Deep-Learning-Labs/
│
├── Lab1/
├── Lab2/
├── Lab3/
├── Lab4/
├── Lab5/
├── Lab6/
├── Lab7/
│
└── README.md
- Clone the repository
git clone https://github.com/your-username/deep-learning-labs.git
- Navigate to the project folder
cd deep-learning-labs
- Open the notebooks or run the Python files for each lab.
The objective of this repository is to demonstrate understanding and implementation of core deep learning concepts through practical laboratory exercises.