With the techniques in Chapter 4, Identifying Anomalous Data and Chapter 5, Training Deep Prediction Models, you should be able to set up and use deep auto-encoders to learn features in data, identify outliers or anomalous values, and deep-feed forward neural networks to predict new outcomes or classify data, such as images, speech, or other data. Although just an introduction, the ideas and code from this book can get you started using deep learning to solve real-world, practical problems.

Deep learning and artificial intelligence are very active areas of research. New tools and techniques are coming out all the time and this book has only provided an introduction to some of the standard and commonly used models in deep learning. It is an exciting time to learn about this field, and I hope that this book has helped you begin your journey.