Mobile Artificial Intelligence Projects
Karthikeyan NG Arun Padmanabhan Matt R. Cole更新时间：2021-06-24 15:52:19
最新章节：Leave a review - let other readers know what you thinkcoverpage
Title Page
Copyright and Credits
Mobile Artificial Intelligence Projects
Dedication
About Packt
Why subscribe?
Packt.com
Contributors
About the authors
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Artificial Intelligence Concepts and Fundamentals
AI versus machine learning versus deep learning
Evolution of AI
The mechanics behind ANNs
Biological neurons
Working of artificial neurons
Scenario 1
Scenario 2
Scenario 3
ANNs
Activation functions
Sigmoid function
Tanh function
ReLU function
Cost functions
Mean squared error
Cross entropy
Gradient descent
Backpropagation – a method for neural networks to learn
Softmax
TensorFlow Playground
Summary
Further reading
Creating a Real-Estate Price Prediction Mobile App
Setting up the artificial intelligence environment
Downloading and installing Anaconda
Advantages of Anaconda
Creating an Anaconda environment
Installing dependencies
Building an ANN model for prediction using Keras and TensorFlow
Serving the model as an API
Building a simple API to add two numbers
Building an API to predict the real estate price using the saved model
Creating an Android app to predict house prices
Downloading and installing Android Studio
Creating a new Android project with a single screen
Designing the layout of the screen
Adding a functionality to accept input
Adding a functionality to consume the RESTful API that serves the model
Additional notes
Creating an iOS app to predict house prices
Downloading and installing Xcode
Creating a new iOS project with a single screen
Designing the layout of the screen
Adding a functionality to accept input
Adding a functionality to consume the RESTful API that serves the model
Additional notes
Summary
Implementing Deep Net Architectures to Recognize Handwritten Digits
Building a feedforward neural network to recognize handwritten digits version one
Building a feedforward neural network to recognize handwritten digits version two
Building a deeper neural network
Introduction to Computer Vision
Machine learning for Computer Vision
Conferences help on Computer Vision
Summary
Further reading
Building a Machine Vision Mobile App to Classify Flower Species
CoreML versus TensorFlow Lite
CoreML
TensorFlow Lite
What is MobileNet?
Datasets for image classification
Creating your own image dataset using Google images
Alternate approach of creating custom datasets from videos
Building your model using TensorFlow
Running TensorBoard
Summary
Building an ML Model to Predict Car Damage Using TensorFlow
Transfer learning basics
Approaches to transfer learning
Building the TensorFlow model
Installing TensorFlow
Training the images
Building our own model
Retraining with our own images
Architecture
Distortions
Hyperparameters
Image dataset collection
Introduction to Beautiful Soup
Examples
Dataset preparation
Running the training script
Setting up a web application
Summary
PyTorch Experiments on NLP and RNN
PyTorch
The features of PyTorch
Installing PyTorch
PyTorch basics
Using variables in PyTorch
Plotting values on a graph
Building our own model network
Linear regression
Classification
Simple neural networks with torch
Saving and reloading data on the network
Running with batches
Optimization algorithms
Recurrent neural networks
The MNIST database
RNN classification
RNN cyclic neural network – regression
Natural language processing
Affine maps
Non-linearities
Objective functions
Building network components in PyTorch
BoW classifier using logistic regression
Summary
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
WaveNet
Architecture
Network layers in WaveNet
The algorithm's components
Building the model
Dependencies
Datasets
Preprocessing the dataset
Training the network
Testing the network
Transforming a speech WAV file into English text
Getting the model
Bazel build TensorFlow and quantizing the model
TensorFlow ops registration
Building an Android application
Requirements
Summary
Implementing GANs to Recognize Handwritten Digits
Introduction to GANs
Generative versus discriminative algorithms
How GANs work
Understanding the MNIST database
Building the TensorFlow model
Training the neural network
Building the Android application
Digit classifier
Summary
Sentiment Analysis over Text Using LinearSVC
Building the ML model using scikit–learn
Scikit-learn
The scikit-learn pipeline
LinearSVC
Building the iOS application
Summary
What is Next?
Popular ML–based cloud services
IBM Watson services
Microsoft Azure Cognitive Services
Vision APIs
Speech APIs
Knowledge APIs
Search APIs
Language APIs
Amazon ML
Vision services
Chat services
Language services
Google Cloud ML
Building your first ML model
The limitations of building your own model
Personalized user experience
Providing better search results
Targeting the right user
Summary
Further reading
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更新时间：2021-06-24 15:52:19