If you're a little confused about the buzz words machine learning, deep learning, machine intelligence, and artificial intelligence (AI), here's a quick summary: machine intelligence and AI are really just the same thing; machine learning is a field, also the most popular one, of AI; deep learning is one special type of machine learning, and is also the modern and most effective approach to solving complicated problems such as computer vision, speech recognition and synthesis, and natural language processing. So in this book, when we say AI, we primarily mean deep learning, the savior that took AI from the long winter to the summer. For more information about the AI winter and deep learning, you can check out [https://en.wikipedia.org/wiki/AI_winter](https://en.wikipedia.org/wiki/AI_winter) and [http://www.deeplearningbook.org](http://www.deeplearningbook.org).
An alternative to setting up your own GPU-powered Ubuntu with TensorFlow is to use TensorFlow in a GPU-enabled cloud service such as Google Cloud Platform's Cloud ML Engine ([https://cloud.google.com/ml-engine/docs/using-gpus](https://cloud.google.com/ml-engine/docs/using-gpus)). There are pros and cons of each option. Cloud services are generally time-based billing. If your goal is to train or retrain models to be deployed on mobile devices, meaning the models are not super complicated, and if you plan to do machine learning training for a long time, it'd be more cost effective and satisfying to have your own GPU.