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README.md

README.md

360|iDev 2017

360|iDev Schedule

Sunday (August 13)

Workshop 1: BUILD YOUR OWN CUSTOM MUSICAL INSTRUMENT

In this workshop we utilized AudioKit to build some custom soundboards. We learned about some of the features of the audio support for iOS.

Workshop 2: DEVELOPING FOR APPLE WATCH WITH SWIFT

We built a watch app from scratch (a pomodoro timer):

  • IB for Watch apps (the only way to build them, no code only VCs)
  • Wired up the app itself with a "settings" menu (to configure the length of time)
  • Wired up notifications (using UNNotificationCenter)
  • Created a complication (Extra large only due to time constraints)
  • moved the supported watch version back to add support for older WatchOS versions

XCODE AND FRIENDS

Monday (August 14)

HealthKit

  • Presenter: Igor Kantor
  • Folder: 03_HealthKit
  • https://github.com/Iiiggs/AsthmaBuddy - reference app
  • https://github.com/ResearchKit/ResearchKit - ResearchKit
  • HKUnit - really useful for unit conversions
  • HKWorkout - for reporting workouts
  • HKQuery Types:
    • HKAnchoredObjectQuery
    • HKCorrelationQuery
    • HKObserverQuery
    • HKSampleQuery
    • HKSourceQuery
    • HKStatisticsQuery
    • HKStatisticsCollectionQuery
  • ResearchKit Chart Control: ORKLineGraphChartView

BEYOND BYTE: PROTOTYPING WITH IPAD PLAYGROUNDS

EXPLORING NEURAL NETWORKS FOR RECOGNITION OF HAND-DRAWN SHAPES

Chris presented "Napkin Demo", gesture-based demo on the mac that recognizes a few "shapes"

  • Neuron

    • a node that sums incoming signals and outputs a signal
    • activation function controls output based on the inputs
  • Synapse

    • Connections between neurons
    • Synapse scales or weights the input traveling along it
  • Sigmoid function is useful for "bucketing" outputs

  • Feed Forward Network - no feedback

  • Simple model: inputs, 1 hidden layer, outputs

  • Back propagation and gradient descent

  • Youtube has videos on Gradient Descent and Back Propagation

  • http://playground.tensorflow.org

  • Book: Make your own Neural Network (Rashid, Tariq)

  • Capturing the drawing

    • UIPanGestureRecognizer
    • Connect the points in a quadratic BezierPath (normalizing the input)
  • WWDC 2012 Session: Building Advanced Gesture Recognizers

  • Strategy

    • Capture drawing as an image (crop to just the path bounds, normalize the aspect)
    • Scale the image down (just enough pixels to capture the essential features, restroke the path so stroke width is invariant)
    • Convert to Grayscale (RGB would me we'd need 3 input nodes for each pixel)
    • Network expect inputs between (0.0, 1.0) - clamping

DYNAMIC TYPOGRAPHY & ADAPTIVE LAYOUTS IN IOS 11

  • Presenter: Sean Coleman
  • Slides: https://speakerdeck.com/bjtitus/creating-adaptable-layouts
  • Font Pairing:
    • Roboto Slab / San Francisco Pro Text (example)
  • San Francisco was designed with Accessibility in mind.
    • Best font to use with dynamic typing
  • IOS6: Auto Layout
  • IOS7: Dynamic Type
  • IOS8: Adaptive Layout
  • IOS9: San Francisco font / UIStackView
  • IOS10: Automatically Adjust Font / preferredContentSizeCategory
  • IOS11: Dynamic Type for custom fonts / Adaptive Layout is ready
  • Accessibility Inspector (Xcode9) tool can interact with the simulator (change text size)!
  • Common issue: Style Guides != Text Styles
    1. Persuade your stakeholders
    2. Scale (scale the font size based on system scale)
    3. Lookup table
  • Adaptive Layouts
    • Show the NYT paper embedded in a iOS Screen 🤣
  • Be a platform advocate!

EVENT DELIVERY IN IOS

ADVANCED DEBUGGING WITH XCODE – EXTENDING LLDB

ADVANCED DATES AND TIMES IN SWIFT

ADVANCED BUILDING APPS FOR APPLE TV

FROM IOS TO IOT: THE EXPERIENCES OF A SOFTWARE GUY BUILDING HARDWARE

Tuesday (August 15)

PRACTICAL SECURITY

LEARNING FROM IOS ANIMATIONS

PLAYING NICE WITH DESIGN

CREATING ADAPTABLE LAYOUTS USING TRAIT COLLECTIONS AND CONTAINER VIEWS

DEEP LEARNING ON IOS

  • Presenter: Shuichi Tsutsumi
  • Slides: https://www.slideshare.net/t26v0748/deep-learning-on-ios
  • Sample Code: https://github.com/shu223/iOS-10-Sampler
  • Github: https://github.com/shu223 (iOS Samplers)
  • How to implement "Deep Learning" on iOS
  • "Pose Estimation" example
  • Demo - app that recognizes over 1000 types of objects (while in airplane mode)
    • Pre-trained models (inference) take considerably less CPU and can run on a mobile device
  • CoreML (iOS11)
    • Uses Metal Performance Shaders (MPSCNN) - GPU - iOS10
    • Uses Accelerate (BNNS) - CPU - iOS10
  • 3 steps
    1. Create the Model
      • What tools can be used for the training?
        • TensorFlow
        • Caffe
        • Keras
        • Any other format can be used as long as it can be read by the app: .dat, .hdf5
    2. Implementing the Network
      • Classes corresponding to each CNN layers are provided:
        • MPSCNNConvolution (Convolution)
        • MPSSCNNPooling (Pooling)
        • MPSCNNFullyConnected (Fully Connected)
      • Complicated math or GPU optimization are encapsulated
    3. Implement the inference
      • MPSImage: Input Image
      • CNN: trained params are loaded
      • Result
  • Demo: Swift Logo detection
    • Trained the model using Tensor Flow
    • 2000 lines of code for the shader implementation
    • CoreML:
      1. Convert w/ coremltools
      2. Drag & Drop model -> xcode -> generate Swift
  • Vision framework sits on top of CoreML
    • You don't have to touch metal to use the vision framework
  • You should generally choose MPSCNN (GPU) over BNNS (CPU) for ML

TensorFlow on iOS

  • Presenter: Taylan Pince @tylanpince
  • Slides: https://speakerdeck.com/taylanpince/tensorflow-neural-networks-on-ios
  • Links:
  • Interesting (Related) Projects
    • ImageNet - open DB of pre-tagged images
    • Kaggle - https://www.kaggle.com/ - The Home of Data Science & Machine Learning
  • Very much "trial and error", not "science"
  • 10% of your "data" should be used for training data, and the remaining for testing
    • don't cross the streams!
  • Tensorflow
    • produces a pb file
    • C++ Library
    • Adds about 40MB to final binary size
    • Can't use bitcode
    • Cannot use GPU (uses CPU)
    • Tensorflow - use freeze_graph & optimize_for_inference
    • Import final pb file into Xcode project
  • CoreML
    • Pretrained Models
      • Inception v3
      • VGG16
      • MobileNet
      • SqueezeNet
    • Custom Models
      • Convert Caffe or Keras models with coremltools
      • import mlmodel into your xcode project
  • Metal
    • Low-level API behind CoreML
    • Always runs on GPU
    • Got tons of love with iOS11 updates
    • Apple has the full inception v3 model implemented in model as sample code
    • Convert pb file into a binary metal can read
  • Recap
    • Train with TensorFlow + Keras
    • Use CoreML if you can
    • Use TF if you need multi-platform
  • Lots of Apple Samples

Life Beyond Storyboards

  • Presenter: Ryan Poolos
  • Slides:
  • Layout Anchors

IMPLEMENTING CONTINUOUS DELIVERY WITH IOS

OVERCOMING THE STRESS SURROUNDING CODE REVIEW, FOR THE BETTERMENT OF YOUR PROJECT AND CAREER

THREADS, QUEUES, AND THINGS TO COME: THE PRESENT AND FUTURE OF CONCURRENCY IN SWIFT

IF YOU CAN LEARN IT, YOU CAN TEACH IT

STARTING, GROWING AND RUNNING A SUCCESSFUL DEVELOPER MEETUP

THE RIGHT 🛠 S FOR THE JOB

NOTIFICATION HANDLING ON WATCHKIT

ADVANCED DRAWING TECHNIQUES WITH UIBEZIERPATH AND APPLE PENCIL

Wednesday (August 16)

ADVANCED BUILDING APPS FOR APPLE WATCH

HOW I LEARNED TO STOP WORRYING AND LOVE AUTOLAYOUT

HOW OPEN SOURCE DEVELOPMENT CAN MAKE YOU A BETTER DEVELOPER

THE INTERNET OF THINGS AND IOS: DON’T LET YOUR TOASTER BRING DOWN THE INTERNET!

THE LATEST IN DEVELOPING FOR WATCHOS

CONTRIBUTING TO SWIFT

LEGAL ISSUES IN IOS DEVELOPMENT

REVERSE ENGINEERING THE IOS SIMULATOR’S SPRINGBOARD

This talk was a hands-on demo where Derek used LLDB + his own suite of tools (see the git repo above and Facebook's chisel for more detail) to hack springboard. You need to be a bit more than a script-kiddy to be able to do what he does here. Great demo!