Machine Learning Projects for Mobile Applications
Karthikeyan NG更新时间：2021-06-10 19:42:11
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Title Page
Dedication
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Contributors
About the author
About the reviewer
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Preface
Who this book is for
What this book covers
To get the most out of this book
Download the code
Download the color images
Conventions used
Get in touch
Reviews
Mobile Landscapes in Machine Learning
Machine learning basics
Supervised learning
Unsupervised learning
Linear regression - supervised learning
TensorFlow Lite and Core ML
TensorFlow Lite
Supported platforms
TensorFlow Lite memory usage and performance
Hands-on with TensorFlow Lite
Converting SavedModel into TensorFlow Lite format
Strategies
TensorFlow Lite on Android
Downloading the APK binary
TensorFlow Lite on Android Studio
Building the TensorFlow Lite demo app from the source
Installing Bazel
Installing using Homebrew
Installing Android NDK and SDK
TensorFlow Lite on iOS
Prerequisites
Building the iOS demo app
Core ML
Core ML model conversion
Converting your own model into a Core ML model
Core ML on an iOS app
Summary
CNN Based Age and Gender Identification Using Core ML
Age gender and emotion prediction
Age prediction
Gender prediction
Convolutional Neural Networks
Finding patterns
Finding features from an image
Pooling layer
Rectified linear units
Local response normalization layer
Dropout layer
Fully connected layer
CNNs for age and gender prediction
Architecture
Training the network
Initializing the dataset
The implementation on iOS using Core ML
Summary
Applying Neural Style Transfer on Photos
Artistic neural style transfer
Background
VGG network
Layers in the VGG network
Building the applications
TensorFlow-to-Core ML conversion
iOS application
Android application
Setting up the model
Training your own model
Building the application
Setting up the camera and an image picker
Summary
References
Deep Diving into the ML Kit with Firebase
ML Kit basics
Basic feature set
Building the application
Adding Firebase to our application
Face detection
Face orientation tracking
Landmarks
Classification
Implementing face detection
Face detector configuration
Running the face detector
Step one: creating a FirebaseVisionImage from the input
Using a bitmap
From media.Image
From a ByteBuffer
From a ByteArray
From a file
Step two: creating an instance of FirebaseVisionFaceDetector object
Step three: image detection
Retrieving information from detected faces
Barcode scanner
Step one: creating a FirebaseVisionImage object
From bitmap
From media.Image
From ByteBuffer
From ByteArray
From file
Step two: creating a FirebaseVisionBarcodeDetector object
Step three: barcode detection
Text recognition
On-device text recognition
Detecting text on a device
Cloud-based text recognition
Configuring the detector
Summary
A Snapchat-Like AR Filter on Android
MobileNet models
Building the dataset
Retraining of images
Model conversion from GraphDef to TFLite
Gender model
Emotion model
Comparison of MobileNet versions
Building the Android application
References
Questions
Summary
Handwritten Digit Classifier Using Adversarial Learning
Generative Adversarial Networks
Generative versus discriminative algorithms
Steps in GAN
Understanding the MNIST database
Building the TensorFlow model
Training the neural network
Building the Android application
FreeHandView for writing
Digit classifier
Summary
Face-Swapping with Your Friends Using OpenCV
Understanding face-swapping
Steps in face-swapping
Facial key point detection
Identifying the convex hull
Delaunay triangulation and Voronoi diagrams
Affine warp triangles
Seamless cloning
Building the Android application
Building a native face-swapper library
Android.mk
Application.mk
Applying face-swapping logic
Building the application
Summary
References
Questions
Classifying Food Using Transfer Learning
Transfer learning
Approaches in transfer learning
Training our own TensorFlow model
Installing TensorFlow
Training the images
Retraining with own images
Training steps parameter
Architecture
Distortions
Hyperparameters
Running the training script
Model conversion
Building the iOS application
Summary
What's Next?
What you have learned so far
Where to start when developing an ML application
IBM Watson services
Microsoft Azure Cognitive Services
Amazon ML
Google Cloud ML
Building your own model
Limitations of building your own model
Personalized user experience
Better search results
Targeting the right user
Summary
Further reading
Other Books You May Enjoy
Leave a review - let other readers know what you think
更新时间：2021-06-10 19:42:11