Summary
In this chapter, you learned how to build neural networks using Keras, one of the best deep learning libraries in the world. You built Sequential dense models with a variety of hidden layers and nodes using the ReLU activation function and the Adam optimizer. You used Early Stopping to find an ideal number of epochs, and you used Dropout to help prevent overfitting. Furthermore, you trained both regressors and classifiers, making sure to use binary_crossentropy as the classification loss function and the sigmoid activation function. Additionally, you learned about the foundations behind convolutions and built convolutional neural networks to classify handwritten digits with over 98% accuracy.
Congratulations on completing your deep learning journey.
The next chapter is the final chapter of the second edition of the Python Workshop, New Features in Python, which includes updates from Python 3.7 to Python 3.11.
