Deep Learning
Program Project Program Identification Program ID: T5 Program Title: Deep Learning Module Program Information: Project Title: Deep Learning for Handwritten Digit Recognition using MNIST Dataset Description Project Overview: The project aims to provide trainees with practical experience in deep learning techniques for handwritten digit recognition using the MNIST dataset. Trainees will explore various aspects of deep learning, including model design, hyperparameter tuning, addressing overfitting, feature transformation, and visualization techniques. Group members: Maximum three members allowed Dataset: MNIST Dataset The MNIST dataset is a well-known benchmark dataset in the field of machine learning, consisting of 28×28 pixel grayscale images of handwritten digits (0 to 9). Dataset Link: http://yann.lecun.com/exdb/mnist/ Tasks and Instructions: 1. Data Loading and Preprocessing (0 marks): 1 – V 1.0/05032024 • • 2. • • 3. • Download the MNIST dataset and load it into your preferred development environment (e.g., Python). Preprocess the data by normalizing pixel values and splitting it into training and testing sets. Baseline Model with Traditional Machine Learning Algorithms (15 marks): Implement a baseline model using traditional machine learning algorithms such as logistic regression. Evaluate the baseline model’s performance using appropriate evaluation metrics. Ensemble of Machine Learning Algorithms (20 marks): Create an ensemble of three different machine learning algorithms (e.g., Decision Trees, Random Forest, Support Vector Machines) and train them on the MNIST dataset. • Combine the predictions of individual models using techniques such as averaging or voting. • Evaluate the ensemble model’s performance and compare it with the baseline model. 4. Neural Network Model Design (20 marks): • Design and implement multiple neural network architectures for handwritten digit recognition. • Experiment with different network architectures, including variations in the number of layers, neurons per layer, activation functions, and regularization techniques. • Train each neural network model on the MNIST dataset and evaluate its performance and report overfitting. 5. Hyperparameter Tuning and Overfitting Mitigation (20 marks): • Perform hyperparameter tuning for the best-performing neural network architecture using techniques like Grid Search or Random Search. • Implement strategies to mitigate overfitting in neural networks, such as dropout regularization or early stopping. • Evaluate the tuned model’s performance and compare it with the baseline and ensemble models. 6. Feature Transformation and Visualization (15 marks): • Apply Principal Component Analysis (PCA) to transform the original data into a lower-dimensional space. • Train a neural network model on the PCA-transformed data and compare its performance with the model trained on the original data. • Visualize the high-dimensional MNIST data in a 2D space using t-Distributed Stochastic Neighbor Embedding (t-SNE) and interpret the results. 7. Documentation and Present (10 marks): • Present the project process, including data preprocessing, model implementation, hyperparameter tuning, and visualization techniques. • Prepare a one page report summarizing the key findings, insights, and lessons learned from the project. Total Marks: 100 Note: • • • • Trainees are encouraged to experiment with different deep learning architectures, hyperparameters, and techniques to gain a deeper understanding of the subject matter. Collaboration, knowledge sharing, and seeking guidance from instructors and peers are highly encouraged throughout the project. Ensure adherence to best practices in deep learning model development, including proper data preprocessing, model evaluation, and documentation. The project aims to provide a comprehensive learning experience in deep learning techniques for image classification tasks using a well-known benchmark dataset. 2 – V 1.0/05032024 Project Outcomes By the end of this project trainee will deliver: A. Python code in a Notebook with Markdown documentation B. Presenting your algorithms results comparison and insights in last section of the notebook. 3 – V 1.0/05032024
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