Building and Training a Simple Artificial Neural Network (ANN) for Handwritten Digit Classification using the MNIST Dataset

Title: Building a Simple Neural Network to Classify Handwritten Digits Using the MNIST Dataset

Deep learning, a subset of machine learning and artificial intelligence, has revolutionized the field of computer vision. In this blog post, we explore the process of building a simple artificial neural network (ANN) to classify handwritten digits using the famous MNIST dataset.

The MNIST dataset, consisting of 70,000 grayscale images of handwritten digits from 0 to 9, is a cornerstone in machine learning and computer vision. Divided into training and test sets, this dataset serves as the perfect playground for training deep learning models.

Using the Keras library, we effortlessly load the MNIST dataset and preprocess it for our neural network. By reshaping the data and normalizing pixel values, we prepare the dataset for training.

Building our neural network involves creating layers for input, hidden, and output, each with specific configurations and activation functions. We compile the model with an optimizer, loss function, and metrics before training it on the training data.

After training, we evaluate the model’s performance on the test data and make predictions on unseen images. To enhance our model, we add a hidden layer with more neurons and a different activation function, improving its accuracy.

To gain insights into our model’s performance, we create a confusion matrix and visualize it, showcasing the model’s ability to correctly classify handwritten digits.

In conclusion, this blog post highlights the step-by-step process of building and improving a deep learning model for digit classification. By understanding the fundamentals of deep learning, we can tackle complex machine learning problems with confidence and precision.