Swift for TensorFlow in 3 hours
Who is this presentation for?ML programmers who want to learn new skills, deep learning engineers, Python programmers seeking to diversify, users of TensorFlow wanting to learn Swift for TensorFlow, and anyone who wants to get into deep learning or Swift.
Isn’t Swift just for app developers? No! Swift for TensorFlow1 provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries, as needed) and all the advantages of Swift, the safe, fast, incredibly capable open source programming language.
This tutorial will equip you with the to knowledge to use Swift, a programming language that’s great for everything from numeric computing to application development, and Swift for TensorFlow, the official TensorFlow project that brings new tooling, systems design, compilers, and features to the machine learning world, by way of Swift.
- the basics of Swift, and how to get started using a Jupyter Notebook (yes, they fully support Swift!)
- why Swift is a great language for scientific computing and deep learning
- how Swift can match the performance of manually-tuned assembly code in numerical computing
- how Swift for TensorFlow works, what it’s capable of, and where it’s headed (learn the power of differential operators, and being able to ask your types for their gradient!)
- why Swift for TensorFlow is not just a port of TensorFlow to a different language (embrace differentiable programming!)2
- how you can use all your favourite Python libraries, including numpy, pickle, and beyond, easily and directly from Swift (!!!)
Specifically, this tutorial will:
- begin with a Swift programming tutorial, covering how to use Swift, as a programming language (we’ll use both Apple’s playgrounds, if you have a macOS device, as well as Jupyter notebooks)
- move to a Swift for TensorFlow tutorial, exploring fundamental machine learning problem solving using TensorFlow and Swift
- explore and demonstrate common use cases for TensorFlow, using Swift
- demonstrate the use of numpy, the ubiquitous Python library, from Swift, to perform common and useful data science operations, and integrate the results with Swift for TensorFlow
- conclude be bringing all the components together: Swift programming (in a notebook), showcasing common ML problem-solving processes using Swift for TensorFlow, and integrating Python libraries as needed in a pragmatic manner
At the end, we’ll point to resources to build on your journey through Swift, Swift for TensorFlow, and the future of deep learning, differentiable programming, and the future of programming languages.
Swift is a powerful, well supported, open, and now mature programming language. Swift for TensorFlow is brand new, solidly backed, and maturing rapidly. You can’t do everything with Swift for TensorFlow yet, but you can learn a lot, and improve your thinking on how and why certain toolsets work. Learn the bleeding edge before it arrives, and pick up valuable Swift skills along the way.
This tutorial will be a 3-hour exploration of everything you need to know to work with Swift, Swift for TensorFlow, and beyond.
Prerequisite knowledgeNothing special. Python skills would be helpful but are not necessary at all. This will be useful to new programmers and old programmers alike, and people with zero exposure to TF and those who use it daily.
Materials or downloads needed in advance
What you'll learn
University of Tasmania
Marina Rose Geldard, more commonly known as Mars, is a technologist from Down Under in Tasmania. Entering the world of technology relatively late as a mature-age student, she has found her place in the world: an industry where she can apply her lifelong love of mathematics and optimization. When she isn’t busy being the most annoyingly eager graduate student ever, she compulsively volunteers at industry events, conducts research, and serves on the executive committee for her state’s branch of the Australian Computer Society (ACS) as well as the AUC. She’s currently writing Practical Artificial Intelligence with Swift for O’Reilly and working on machine learning projects to improve public safety through public CCTV cameras in her hometown of Hobart.
Dr Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn’t busy avoiding being found out as a fraud, Tim spends most of his time designing and creating little apps and games he won’t let anyone see. He also spent a disproportionately long time writing this tiny little bio, most of which was taken up trying to stick a witty sci-fi reference in. . .before he simply gave up. He’s currently writing ‘Practical AI with Swift’ for O’Reilly Media.
Paris Buttfield-Addison is cofounder of Secret Lab, a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award-winning ABC Play School iPad games, the BAFTA- and IGF-winning Night in the Woods, the Qantas airlines Joey Playbox games, and the Yarn Spinner narrative game framework. Previously, Paris was mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, law, machine learning, and human-centered technology research and writes technical books on mobile and game development (more than 20 so far) for O’Reilly. He holds a degree in medieval history and a PhD in computing. He’s currently writing “Practical AI with Swift” for O’Reilly Media. You can find him on Twitter @parisba
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