This tutorial will guide you through the process of building a basic neural network to classify handwritten digits using the MNIST dataset. The MNIST dataset is a large database of handwritten digits commonly used for training various image processing systems.

Overview

  • MNIST Dataset: A dataset containing 60,000 training images and 10,000 testing images of handwritten digits.
  • Neural Network: A series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
  • TensorFlow: An open-source machine learning framework developed by Google Brain.

Getting Started

Before you begin, make sure you have the following prerequisites installed:

  • Python
  • TensorFlow
  • NumPy

You can install TensorFlow using pip:

pip install tensorflow

Building the Neural Network

Here's a simple example of a neural network for the MNIST dataset:

import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.2),
    layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(train_images, train_labels, epochs=5)

# Evaluate the model
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)

Further Reading

For more detailed information and advanced tutorials, please visit the TensorFlow website.

Image

Here's an example of a handwritten digit from the MNIST dataset:

Handwritten Digit