20 Conclusions

 

This chapter covers

  • Important takeaways from this book
  • Resources for learning further and applying your skills in practice

We’ll start with a bird’s-eye view of what you should take away from this book. This should refresh your memory regarding some of the concepts you’ve learned. Next, I’ll give you a short list of resources and strategies for learning further about machine learning and staying up to date with new advances.

Becoming an effective AI practitioner is a journey, and finishing this book is merely your first step on it. I want to make sure you realize this, and are properly equipped to take the next steps of this journey on your own.

20.1 Key concepts in review

This section briefly synthesizes key takeaways from this book. If you ever need a quick refresher to help you recall what you’ve learned, you can read these few pages.

20.1.1 Various approaches to AI

First of all, deep learning isn’t synonymous with AI, or even with machine learning.

20.1.2 What makes deep learning special within the field of machine learning

20.1.3 How to think about deep learning

20.1.4 Key enabling technologies

20.1.5 The universal machine-learning workflow

20.1.6 Key network architectures

20.2 Limitations of deep learning

20.3 What might lie ahead

20.4 Staying up to date in a fast-moving field

20.4.1 Practice on real-world problems using Kaggle

20.4.2 Read about the latest developments on arXiv

20.4.3 Explore the Keras ecosystem

20.5 Final words