DeepLearningAssignments

Welcome to DeepLearningAssignments! This repository contains four deep learning assignments that cover advanced topics in neural networks, machine learning, and optimization techniques.

Assignments:

1. Deep Dive into RNNs and Gradient Issues

• Explore the fundamental challenges of Recurrent Neural Networks (RNNs), particularly the issues of vanishing and exploding gradients.
• This assignment dives into the architecture of RNNs and demonstrates how these gradient problems can impact training and performance.

2. LSTM Architecture and Backpropagation Through Time (BPTT)

• Learn about Long Short-Term Memory (LSTM) networks, a solution to the gradient issues in RNNs.
• Understand the process of Backpropagation Through Time (BPTT) and how it helps in training deep learning models on sequential data.

3. Hyperparameter Tuning and Regularization in Advanced Machine Learning

• This assignment focuses on the art of hyperparameter tuning and regularization techniques such as L1, L2 regularization, and Dropout.
• Learn how to improve model performance and prevent overfitting.

4. Advanced Optimization Techniques for Deep Learning

• Explore advanced optimization methods like Adam, Adagrad, and RMSprop.
• Learn how these techniques improve the training process for deep learning models, ensuring better convergence and faster training times.

Folder Structure:

Each assignment is structured as a separate directory, with necessary scripts, Jupyter notebooks, and supporting files:

DeepLearningAssignments/
├── Assignment-1-RNNs-Gradient-Issues/
├── Assignment-2-LSTM-BPTT/
├── Assignment-3-Hyperparameter-Tuning-ML/
├── Assignment-4-Advanced-Optimization-DL/
└── README.md             # Main repository README

Getting Started:

1. Clone the repository to your local machine:

   git clone https://github.com/Dinakarnayak/DeepLearningAssignments.git

2. Navigate to a specific assignment folder:

   cd Assignment-1-RNNs-Gradient-Issues

3. Install required dependencies using pip:

   pip install -r requirements.txt

4. Run the Python script or Jupyter notebook to start experimenting with the models:

   python rnn_model.py

License:

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact:

Developed by Dinakar Nayak N.
For inquiries, please reach out at dinakarnayak4248@gmail.com.

LinkedIn: Dinakar Nayak N

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Thank you for exploring this repository! Feel free to contribute and experiment with the assignments.