Deep Learning Projects

Welcome to my deep learning projects repository! Here, I showcase some exciting deep learning projects that leverage neural networks for image classification. My first project focuses on classifying clothing images using the Fashion MNIST dataset, a challenging dataset for machine learning beginners.

Project: Clothing Image Classification using Deep Learning

Overview:

This project demonstrates the power of deep learning in image classification by classifying images of fashion items such as T-shirts, shoes, pants, and more. Using the Fashion MNIST dataset, the model classifies 10 different categories of clothing, including t-shirts, dresses, coats, sandals, and more. The project utilizes the TensorFlow library to build and train a deep neural network.

Key Concepts:

• Convolutional Neural Networks (CNNs) for image classification
• Deep Learning with TensorFlow and Keras
• Fashion MNIST Dataset: A collection of 60,000 28x28 grayscale images for training and 10,000 for testing.

Project Goals:

• Build a deep neural network (DNN) using TensorFlow.
• Train the model to classify images into different categories.
• Evaluate the model’s accuracy and improve its performance using techniques like regularization, dropout, and data augmentation.

Technologies Used:

• Python
• TensorFlow (Keras API)
• NumPy
• Matplotlib for plotting and visualizing data
• Fashion MNIST Dataset (from TensorFlow)

Steps Taken in the Project:

1. Data Preprocessing: Load and normalize the Fashion MNIST dataset, scaling pixel values to be between 0 and 1.
2. Model Architecture: Built a simple yet efficient neural network model using Dense and Dropout layers.
3. Training: Trained the model on the dataset for 5 epochs and evaluated its performance.
4. Evaluation: Plotted the predictions, compared them with true labels, and analyzed the accuracy.