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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