Machine Learning by Tutorials : Beginning Machine Learning for Apple and iOS

✍ Scribed by raywenderlich Tutorial Team, Alexis Gallagher, Matthijs Hollemans, Audrey Tam, Chris LaPollo

• Year: 2021
• Tongue: English
• Leaves: 586
• Edition: 2
• Category: Library

✦ Synopsis

Learn Machine Learning!

Machine learning is one of those topics that can be daunting at first blush. It's not clear where to start, what path someone should take and what APIs to learn in order to get started teaching machines how to learn.

This is where Machine Learning by Tutorials comes in! In this book, we'll hold your hand through a number of tutorials, to get you started in the world of machine learning. We'll cover a wide range of popular topics in the field of machine learning, while developing apps that work on iOS devices.

Who This Book Is For

This books is for the intermediate iOS developer who already knows the basics of iOS and Swift development, but wants to understand how machine learning works.

Topics covered in Machine Learning by Tutorials

• CoreML: Learn how to add a machine learning model to your iOS apps, and how to use iOS APIs to access it.
• Create ML: Learn how to create your own model using Apple's Create ML Tool.
• Turi Create and Keras: Learn how to tune parameters to improve your machine learning model using more advanced tools.
• Image Classification: Learn how to apply machine learning models to predict objects in an image.
• Convolutional Networks: Learn advanced machine learning techniques for predicting objects in an image with Convolutional Neural Networks (CNNs).
• Sequence Classification: Learn how you can use recurrent neural networks (RNNs) to classify motion from an iPhone's motion sensor.
• Text-to-text Transform: Learn how to use machine learning to convert bodies of text between two languages.

By the end of this book, you'll have a firm understanding of what machine learning is, what it can and cannot do, and how you can use machine learning in your next app!

✦ Table of Contents

About the Cover
What You Need
Book License
Book Source Code & Forums
Chapter 1: Machine Learning, iOS & You
What is machine learning?
Deep learning
ML in a nutshell
Can mobile devices really do machine learning?
Frameworks, tools and APIs
ML all the things?
The ethics of machine learning
Key points
Where to go from here?
Chapter 2: Getting Started with Image Classification
Is that snack healthy?
Core ML
Vision
Creating the VNCoreML request
Performing the request
Showing the results
How does it work?
Multi-class classification
Bonus: Using Core ML without Vision
Challenge
Key points
Chapter 3: Training the Image Classifier
The dataset
Create ML
How we created the dataset
Transfer learning
Logistic regression
Looking for validation
More metrics and the test set
Examining Your Output Model
Recap
Challenge
Key points
Chapter 4: Getting Started with Python & Turi Create
Starter folder
Python
Packages and environments
Installing Anaconda
Useful Conda commands
Setting up a base ML environment
Jupyter Notebooks
Transfer learning with Turi Create
Shutting down Jupyter
Docker and Colab
Key points
Where to go from here?
Chapter 5: Digging Deeper into Turi Create
Getting started
Transfer learning with SqueezeNet
Getting individual predictions
Using a fixed validation set
Increasing max iterations
Confusing apples with oranges?
Training the classifier with regularization
Wrangling Turi Create code
A peek behind the curtain
Challenges
Key points
Chapter 6: Taking Control of Training with Keras
Getting started
Back to basics with logistic regression
Building the model
Loading the data
Training the logistic regression model
Your first neural network
Challenge
Key points
Chapter 7: Going Convolutional
Got GPU?
Convolution layers
Your first convnet in Keras
Key points
Where to go from here?
Chapter 8: Advanced Convolutional Neural Networks
SqueezeNet
MobileNet and data augmentation
How good is the model really?
Converting to Core ML
Challenges
Key points
Chapter 9: Beyond Classification
Where is it?
A simple localization model
Key points
Chapter 10: YOLO & Semantic Segmentation
Single stage detectors
Hello Turi, my old friend
The demo app
Semantic segmentation
Challenges
Key points
Where to go from here?
Chapter 11: Data Collection for Sequence Classification
Building a dataset
Analyzing and preparing your data
Key points
Where to go from here?
Chapter 12: Training a Model for Sequence Classification
Creating a model
Getting to know your model
A note on sequence classification
Key points
Where to go from here?
Chapter 13: Sequence Classification
Classifying human activity in your app
Challenges
Key points
Chapter 14: Natural Language Classification
Getting started
Language identification
Finding named entities
Adding a search feature
Sentiment analysis
Building a sentiment classifier
Custom word classifiers
The remaining bits
Key points
Where to go from here?
Chapter 15: Natural Language Transformation, Part 1
Getting started
The sequence-to-sequence model
Prepare your dataset
Build your model
Train your model
Inference with sequence-to-sequence models
Converting your model to Core ML
Using your model in iOS
Let's talk translation quality
Key points
Where to go from here?
Chapter 16: Natural Language Transformation, Part 2
Bidirectional RNNs
Beam search
Attention
Why use characters at all?
Words as tokens and word embedding
Key points
Where to go from here?
Conclusion
Photo Credits