Course Outline

Introduction

  • Tensorflow vs Tensorflow Lite

Overview of TensorFlow Lite Features and Workflow

  • Recap of machine learning and deep learning concepts
  • How on-device low-latency inference is achieved
  • End-to-end model building and deployment

Preparing the Development Environment

  • Starting a Swift project
  • Adding TensorFlow to the project

Capturing an Image with a Device Camera

  • How camera input is captured
  • Overview of classes and methods
  • Running inference on a frame (performing image classification)

Creating an App for Object Detection

  • Selecting a TensorFlow Model
  • Converting the TensorFlow Model
  • Loading the TensorFlow Model onto a Mobile Device
  • Loading a Pre-trained TensorFlow Model

Creating an App for Image Classification

  • Selecting a TensorFlow Model
  • Converting the TensorFlow Model
  • Loading the TensorFlow Model onto a Mobile Device
  • Loading a Pre-trained TensorFlow Model

Customizing the Model and Data

  • Pre-processing a dataset
  • Setting the hyperparameters

Optimizing the TensorFlow Model

  • Measuring performance against a benchmark
  • Measuring accuracy
  • Retraining a TensorFlow model

Exploring Alternative Models

  • Choosing a different model
  • Training a model to recognize new classes (transfer learning)
  • Obtaining training images for new labels

Deploying the AI Enabled iOS App

  • Performing image classification in the field

Troubleshooting

Summary and Conclusion

Requirements

  • Experience with Swift programming
  • Experience with mobile application development
  • An iOS device running v12 or higher

Audience

  • Developers
  • Data scientists who wish to develop AI-enabled mobile applications on iOS
 21 Hours

Testimonials (4)

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