Rahul Bhalley - Deep Learning with Swift for TensorFlow: Differentiable Programming with Swift

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-6329-7 . For more detailed information, please visit http://www.apress.com/source-code .

As a programmer and student pursuing graduation, I had a lot of trouble understanding deep learning by myself when I started this journey back in 2015. So I decided to write a deep learning programming book that might help people in a similar situation as mine to easily understand deep learning. I have tried to keep the explanation of difficult deep learning concepts simple throughout the book.

But why Swift for deep learning? Swift is a powerful general-purpose differentiable programming language. Swift is a well-researched programming language and the code written in it seems like reading English sentences, making it easy for newcomers to learn programming and even deep learning. Furthermore, Swift is optimized for performance, so researchers can write all deep learning algorithms in a single language with simple syntax.

And what about Pythons wide ecosystem of various libraries? With the Python interoperability feature of Swift for TensorFlow (S4TF), you can use Python libraries within Swift!

For newcomers, I hope this book serves as a good starting point to learn programming and deep learning. And for researchers, I hope this book will make the adoption of Swift for TensorFlow for deep learning research easy.

Were unquestionably in the business of forging the gods.

Pamela McCorduck

Nowadays, artificial intelligence (AI) is one of the most fascinating fields of computer science in addition to quantum computing (Preskill, 2018) and blockchain (Nakamoto, 2008). Hype since the mid-2000s in the industrial community has led to large amounts of investments for AI startups. Globally leading technology companies such as Apple, Google, Amazon, Facebook, and Microsoft, just to name a few, are quickly acquiring talented AI startups from all over the world to accelerate AI research and, in turn, improve their own products.

Consider a portable device like Apple Watch. It uses machine intelligence to analyze your real-time motion sensory data to track your steps, standing hours, swimming reps, sleep time, and more. It also calculates your heart rate from temporal blood color variations underneath your wrists skin, alerts you about your heartbeat irregularities, performs electrocardiography (ECG), measures oxygen consumption in blood (VO max) during exercise, and much more. On the other hand, devices like iPhone and iPad use the LIDAR information from camera sensors to create depth map of surrounding instantly. This information is then combined with machine intelligence to deliver computational photography features such as bokeh effect with adjustable strength, immersive augmented reality (AR) features such as reflection and lighting of surrounding on AR objects, object occlusions when humans enter in the scene, and much more. Personal voice assistant like Siri understands your speech allowing you to do various tasks such as controlling your home accessories, playing music on HomePod, calling and texting people, and more. The machine intelligence technology becomes possible due to fast graphics processing unit (GPU). Nowadays GPU on portable devices are fast enough to process users data without having to send it to the cloud servers. This approach helps in keeping the users data private and hence secure from undesirable exposure and usage (Sharma and Bhalley, 2016). In fact, all the features mentioned above are made available with on-device machine intelligence.