🧠 Classification using Artificial Neural Network (ANN)

📘 Overview

This project demonstrates image classification using an Artificial Neural Network (ANN) built with TensorFlow and Keras.
The dataset was imported directly from keras.datasets. The goal was to understand how neural networks learn and to observe the difference in training speed between CPU and GPU execution.

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🚀 Project Highlights

• Implemented a basic ANN model for image classification.
• Used TensorFlow/Keras for building, training, and evaluating the model.
• Learned how model training speed varies between CPU and GPU.
• Observed that deep learning models train significantly faster on GPUs compared to CPUs.

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🧩 Tech Stack

• Language: Python
• Libraries: TensorFlow, Keras, NumPy, Matplotlib
• Dataset: Imported from keras.datasets (e.g., MNIST or CIFAR-10)

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⚙️ Installation & Setup

1. Clone this repository:

   git clone https://github.com/your-username/image-classification-ann.git
   cd image-classification-ann
   
   🖥️ Performance Note
   

Training the model on a CPU (Intel integrated graphics) was slow, demonstrating how much time deep learning models require without GPU acceleration.

💡 Pro Tip: For deep learning projects, prefer a laptop or system with an NVIDIA GPU that supports CUDA and cuDNN. It makes model training 10–50× faster.

Results

Model successfully classified images from the dataset. Achieved reasonable accuracy after several epochs. Observed significant performance improvement potential with GPU usage.