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
Mars Geldard, Tim Nugent, and Paris Buttfield-Addison are here to prove Swift isn’t just for app developers. Swift for TensorFlow provides the power of TensorFlow with all the advantages of Python (and complete access to Python libraries, as needed) and Swift—the safe, fast, incredibly capable open source programming language.
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 tool sets work. Learn the bleeding edge before it arrives, and pick up valuable Swift skills along the way.
This is a three-hour exploration of everything you need to know to work with Swift, Swift for TensorFlow, and beyond. You’ll leave with the 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)
- How you can use all your favorite Python libraries, including NumPy, pickle, and beyond, easily and directly from Swift
- Begin with a Swift programming tutorial covering how to use Swift as a programming language (Mars, Tim, and Paris use both Apple’s Playgrounds (for macOS devices) and Jupyter notebooks.)
- Explore Swift for TensorFlow by diving into fundamental machine learning problem solving using TensorFlow and Swift
- Explore and demonstrate common use cases for TensorFlow using Swift
- Learn how to use NumPy, the ubiquitous Python library from Swift, to perform common and useful data science operations and integrate the results with Swift for TensorFlow
- Bring 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
- Discover resources to build on during your journey through Swift, Swift for TensorFlow, and the future of deep learning, differentiable programming, and the future of programming languages
- Experience with Python (useful but not required)
Materials or downloads needed in advance
- A laptop with any platform (You'll be provided with a link to Jupyter and optionally a local Docker container to run locally with minimal setup needed.)
What you'll learn
- Learn how to program in Swift in depth, how to use that knowledge in Swift for TensorFlow to perform basic deep learning work, and how to get more knowledge to further both your Swift programming broadly and your use of Swift for TensorFlow specifically
University of Tasmania
Marina Rose Geldard (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. She compulsively volunteers at industry events, dabbles in 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 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.
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 his 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 writing Practical Artificial Intelligence with Swift for O’Reilly and building a game for a power transmission company about a naughty quoll. (A quoll is an Australian animal.)
Paris Buttfield-Addison is a 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 a mobile product manager for Meebo (acquired by Google). Paris particularly enjoys game design, statistics, blockchain, machine learning, and human-centered technology. He researches and writes technical books on mobile and game development (more than 20 so far) for O’Reilly; he recently finished writing Practical AI with Swift and is currently working on Head First Swift. He holds a degree in medieval history and a PhD in computing. Paris loves to bring machine learning into the world of practical and useful. You can find him on Twitter as @parisba.
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