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Complete iOS 11 Machine Learning Masterclass
About this course
03 About Machine Learning (4:29)
About your instructor and course overview (4:14)
03 Activity Use Machine Learning Style Transfer (2:14)
Download all project files
Optional - iOS Fundamentals
About this section - start iOS (0:45)
02 Download and install xcode for iOS 11 (2:32)
03 Get the iOS developer license (2:19)
05 How to install iOS 11 on your iPhone or iPad (2:51)
04 How to use a MAC on Windows PC or Linux (2:14)
01 Use the Xcode interface (6:20)
02 Xcode configuration files (5:38)
Optional - Machine Learning Concepts
04 Recurrent Neural Networks basics RNNs (2:45)
01 what is a neuron and neural network (4:31)
02 Parts of an Artificial Neural Network (4:16)
03 Explanation - Convolutional Neural Network (3:28)
About this section - intro to ML (1:25)
iOS Machine Learning With Photos
About this section - coreML with Photos (0:49)
01 Demo of project using coreML on photos (1:36)
02 About ML model and Neural Networks (2:39)
03 Create the xcode project (4:30)
04 How to get pre-made ML models for iOS (6:15)
05 How to use ML models with images (part 1) (9:39)
06 How to use ML models with images (part 2) (8:15)
07 Programming the VN request callback method (5:23)
08 Testing different ML models (6:03)
09 Exercise with Models with Images input (0:55)
10 solution with Models with Images input (4:05)
coreML All about custom models
About this section - model conversion (1:15)
01 Finding custom ML models (4:17)
02 Converting ML models get Anaconda IDE (4:53)
03 Installing Python libraries for core ML (5:23)
04 Installing Caffe tools for core ML conversion (11:01)
05 Converting scikit model to core ml mlmodel format (13:27)
CoreML with Data Set models
01 intro to Working with Data sets (1:49)
02 create xcode project and add iris model (1:10)
03 ML dataset project User Interface (10:39)
04 Properties and picker delegate methods (8:55)
05 Pickerview data source methods (3:38)
06 Coding prediction for data sets (8:16)
07 Code improvements (6:51)
08 Important data set imodels information (3:35)
Project: coreML with Video Camera
00 About section ML Video (1:11)
01 Demo Live Camera feed prediction (1:27)
02 Create xcode project and add VGG16 model (1:40)
03 Building the user interface (4:17)
04 Video Stream variables setup (5:11)
05 Program camera feed (6:24)
06 Capture image from video stream for ML model (7:43)
07 Programming the ML prediction launch (6:41)
08 Processing the ML model output (4:54)
09 Testing the live camera feed with VGG model (2:31)
Section 8: END: iOS coreML fundamentals
Congratulations (0:48)
Optional - Going the extra mile
Adding-converted-model-metadata (6:01)
01-Get-a-PixelBuffer-from-a-UIImage (2:58)
02-UIImage-PixelBuffer-extension-part-1 (8:28)
03-UIImage-PixelBuffer-extension-part-2 (7:47)
04-Using-the-UIImage-PixelBuffer-with-coreML-prediction (3:49)
Optional - Numerous Model Conversions
Load-Save-Keras-models-and-convert-to-mlmodel (9:00)
01-Vision-Image-Request-parameter-options (7:39)
01-Get-a-Caffee-ML-model-with-weights-and-labels (3:20)
02-CoreML-tools-conversion-code-with-Caffe (9:06)
03-Exporting-Caffe-model-to-mlmodel-format (4:54)
04-Using-the-Caffe-model-with-iOS (2:50)
Advanced Vision Techniques: Face Detection
01-Introduction-to-advanced-ML-with-Vision (1:26)
02-Create-the-user-interface-in-storyboard (4:07)
03-Coding-the-Photo-selection (7:58)
04-Coding-Face-Detection (9:25)
05-Face-Detection-exercise (1:16)
06-Face-Detection-solution (3:48)
Advanced Face and Features Detection
01-About-Advanced-Face-Features (1:44)
07-Locate-face-position-and-area-part-1-of-2 (8:50)
08-Locate-face-position-and-area-part-2-of-2 (8:41)
09-Code-to-detect-Face-features-eyes-nose-lips (9:19)
10-face-features-part-2 (8:24)
11-face-features-part-3 (8:37)
12-face-features-part-4 (3:42)
16-Activity-Solution (2:41)
15-Activity-draw-all-face-features-in-blue (1:16)
Advanced - Text Detection
01-About-Text-Detection-Project (1:40)
02-project-text-recog-part-1 (3:33)
03-project-text-recog-part-2 (5:40)
04-project-text-recog-part-3 (4:25)
05-Activity-Text-Detection (0:45)
06-Solution-Text-Detection (2:10)
06 Coding prediction for data sets
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