Brett Koonce - Image Recognition and Dataset Categorization

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In this book, we are going to learn convolutional neural networks by focusing on the specific problem of image recognition, using Swift for Tensorflow and a command-line Unix approach. If you are new to this field, then I would suggest you read the first few chapters and get a working system bootstrapped and then spend your time going through the basics with MNIST and CIFAR repeatedly, in particular familiarizing yourself with how neural networks work. If you feel comfortable with the core concepts already, then feel free to skip ahead to the middle where we explore some more powerful convolutional neural networks.

The short version is that I believe swift is a modern, open source, beginner-friendly language that has proven itself by solving real problems for iOS developers daily. By integrating automatic differentiation into the programming language, a number of interesting compiler techniques to address the limitations of current machine learning software and hardware become possible in the long term. This is in my opinion where the world is headed, one way or another.

Image recognition is one of the oldest, most well-understood uses of neural networks. As a result, we can introduce the basics and then build up to advanced state-of-the-art approaches in a logically consistent manner. With this foundation, you will be able to branch out to tackle other image-related tasks (e.g., object detection and segmentation) easily. The deep learning techniques needed to build large-scale convolutional neural networks translate easily to reinforcement learning and generative adversarial networks (GANs) , two important areas of modern research. In addition, I believe this foundation will make it easy to make the transition to time sequence models such as recurrent neural networks (RNNs) and long short-term memory (LSTM) once you have mastered CNNs.

Ultimately, by blurring the line between your personal computer and the cloud, my goal is for you to understand that there is fundamentally no difference between doing things locally or remotely. The real limiting factor then is your imagination, not resources.

Doing things this way will be more work at first, I will admit. But once you have mastered this workflow, it will be much easier for you to scale in the future. If you are willing to put in the time now, this approach will make your skills much more flexible and powerful in the future. What you do with them is up to you.

This book is organized as follows.