🧠 Handwritten Digit Recognition using Neural Networks

A deep learning project that classifies handwritten digits from the MNIST dataset using a simple Artificial Neural Network built with TensorFlow and Keras

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📊 Project Overview

This project implements a neural network to recognize handwritten digits (0-9) from the MNIST dataset. The model achieves over 96% accuracy on the test set using a relatively simple architecture with just a few dense layers.

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🧠 Model Architecture

• Input: Flattened 28×28 grayscale images (784 neurons)
• Hidden Layer 1: 128 neurons with ReLU activation function
• Hidden Layer 2: 32 neurons with ReLU activation function
• Output Layer: 10 neurons with Softmax activation (one for each digit)

Total Parameters: 104,938

Model Architecture

model = Sequential()
model.add(Flatten(input_shape=(28, 28)))
model.add(Dense(128, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(10, activation='softmax'))

# Model Summary
"""
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 flatten (Flatten)           (None, 784)               0         
 dense (Dense)               (None, 128)               100480    
 dense_1 (Dense)             (None, 32)                4128      
 dense_2 (Dense)             (None, 10)                330       
=================================================================
Total params: 104,938
Trainable params: 104,938
Non-trainable params: 0
"""