The goal of our project is to create a model that can predict handwritten digits, which is a common problem in the field of machine learning.

To get started, we're going to be using a dataset called MNIST, which is a collection of 70,000 handwritten digits labeled from 0 to 9. We'll use this dataset to train our model to recognize different digits and make predictions on new, unseen images.

We'll design our neural network architecture using TensorFlow's Keras API. This is a really cool tool that allows us to build complex neural networks with just a few lines of code. Our network will consist of multiple layers, including a convolutional layer, a pooling layer, and a dense layer. These layers will be used to extract relevant features from the images and classify them into the correct digit category.

Once we've designed our model, we'll train it using the training dataset and evaluate its performance on the testing dataset. This will help us see how well our model is able to generalize to new, unseen images. We'll also fine-tune our model by adjusting the hyperparameters and repeating the training process until we achieve satisfactory accuracy.

By the end of this course, you'll have a functional deep-learning model that can accurately predict handwritten digits. You will build a fully functional neural network using Google Colab notebook. Once you are done with the course, you can share the links to your notebook in the class gallery and comment on other's notebooks. I ll add my feedback to your notebooks as well.

Miley Hollenberg

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