1. Getting started
001 About Your Instructor and Course Overview00:00:00
002 About Machine Learning00:00:00
003 Activity_ Playing with Machine Learning Style Transfer00:00:00
2. Optional – iOS Fundamentals
004 About this section – start iOS00:00:00
005 Download and install xcode for iOS 11.mp400:00:00
006 Get the iOS developer license.mp400:00:00
007 How to use a MAC on Windows PC or Linux.mp400:00:00
008 How to install iOS 11 on your iPhone or iPad00:00:00
009 Use the Xcode interface00:00:00
010 Xcode configuration files00:00:00
3. Optional – Machine Learning Concepts
011 About this section – intro to ML00:00:00
012 What is an Artificial Neuron – Neural Network00:00:00
013 Parts of an Artificial Neural Network00:00:00
014 Explanation – Convolutional Neural Network00:00:00
015 Recurrent Neural Networks basics RNNs00:00:00
4. iOS Machine Learning With Photos
016 About this section – coreML with Photos00:00:00
017 Demo of project using coreML on photos00:00:00
018 About ML model and Neural Networks00:00:00
019 Project_ Create the xcode project00:00:00
020 Project_ How to add ML models to xcode projects00:00:00
021 Project_ How to get pre-made ML models for iOS00:00:00
022 Project_ How to use ML models with images (part 1)00:00:00
023 Project_ How to use ML models with images (part 2)00:00:00
024 Project_ Programming the VN request callback method00:00:00
025 Testing different ML models00:00:00
026 Exercise_ Models with Images input00:00:00
027 Solution_ Models with Images input00:00:00
028 Summary_ coreML Vision with Photos00:00:00
5. coreML All about custom models
029 About this section – model conversion00:00:00
030 Project_ Finding custom ML models00:00:00
031 Project_ Converting ML models get Anaconda IDE00:00:00
032 Installing Python libraries for core ML00:00:00
033 Installing Caffe tools for core ML conversion00:00:00
034 Project_ Converting scikit model to core ml mlmodel format00:00:00
6. CoreML with Data Set models
035 Introduction to Working with Data sets00:00:00
036 Project_ Create xcode project and add iris model00:00:00
037 Project_ ML dataset project User Interface00:00:00
038 Project_ Properties and picker delegate methods00:00:00
039 Project_ Pickerview data source methods00:00:00
040 Project_ Coding prediction for data sets00:00:00
041 Project_ Code improvements00:00:00
042 Important data set models information00:00:00
7. Project: coreML with Video Camera
043 About CoreML with Video Camera00:00:00
044 Project_ Create xcode project and add VGG16 model00:00:00
045 Project_ Building the user interface00:00:00
046 Project_ Video Stream variables setup00:00:00
047 Project_ Program camera feed00:00:00
048 Project_ Capture image from video stream for ML model00:00:00
049 Project_ Programming the ML prediction launch00:00:00
050 Project_ Processing the ML model output00:00:00
051 Testing the live camera feed with VGG model00:00:00
8. END: iOS coreML fundamentals
052 Congratulations00:00:00
9. Optional – Going the extra mile
053 Adding converted model metadata00:00:00
054 Get a PixelBuffer from a UIImage00:00:00
055 UIImage PixelBuffer extension (part 1)00:00:00
056 UIImage PixelBuffer extension (part 2)00:00:00
057 coreML prediction using UIImage PixelBuffer00:00:00
10. Optional – Numerous Model Conversions
059 Caffe – Get a Caffe ML model with weights and labels00:00:00
060 CoreML tools conversion code with Caffe00:00:00
061 Exporting Caffe model to mlmodel format00:00:00
062 Caffe – Using the Caffe model with iOS00:00:00
063 Keras – Load Save Keras models and convert to mlmodel00:00:00
064 Vision Image Request parameter options00:00:00
