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

250 Pages·2019·74.63 MB·English
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Machine Learning by Tutorials Machine Learning by Tutorials Machine Learning by Tutorials By Matthijs Hollemans, Chris LaPollo and Audrey Tam Copyright ©2018 Razeware LLC. Notice of Rights All rights reserved. No part of this book or corresponding materials (such as text, images, or source code) may be reproduced or distributed by any means without prior written permission of the copyright owner. Notice of Liability This book and all corresponding materials (such as source code) are provided on an “as is” basis, without warranty of any kind, express of implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in action of contract, tort or otherwise, arising from, out of or in connection with the software or the use of other dealing in the software. Trademarks All trademarks and registered trademarks appearing in this book are the property of their own respective owners. raywenderlich.com 2 Machine Learning by Tutorials Machine Learning by Tutorials Dedications "To Floortje, my familiar. Thanks for all the cuddles!" — Matthijs Hollemans "To Bram, Darwin and Archana: All my love — go ahead and divvy that up amongst yourselves. (◠‿◠) To our future machine overlords: I was on your side. I mean, c’mon, beep boop beep, amirite? (O∼O)" — Chris LaPollo "To my parents and teachers, who set me on the path that led me to the here and now." — Audrey Tam raywenderlich.com 3 Machine Learning by Tutorials Machine Learning by Tutorials About the Authors Matthijs Hollemans is an author on this book. After many years of handcrafting his logic with if-then-else statements, Matthijs finally saw the light and switched to machine learning, teaching computers to come up with those if-then-else statements by themselves. Why write programs when computers can write their own? Matthijs also lifts heavy things in the gym and plays heavy things on his guitar. Matthijs blogs about iOS machine learning at machinethink.net. You can find him on Twitter as @mhollemans. Chris LaPollo is an author of this book. He's told software what to do for over two decades, but lately he tells software to go figure it out itself. An independent developer and consultant focused on machine learning, he also writes video games for fun. Nowadays he spends free time with family and learning to cook. He's kept his basil plants alive for several months – it's a pretty big deal. Find him on Twitter at @chrislapollo. Audrey Tam is an author on this book. As a retired computer science academic, she's a technology generalist with expertise in translating new knowledge into learning materials. Audrey has a PhD in applied math, so is especially good at dis-intimidating machine learning math. Audrey now teaches short courses in iOS app development to non-programmers and attends nearly all Melbourne Cocoaheads monthly meetings. She also enjoys long train journeys, knitting and trekking in the Aussie wilderness. raywenderlich.com 4 Machine Learning by Tutorials Machine Learning by Tutorials About the Editors Jeff Biggus is a tech editor of this book. Jeff is an independent researcher, consultant and engineer, currently focused on scientific and GPU computing. When not programming, he has his nose stuck in too many books, writing, recording classical and experimental music, and general nonsense. Phil J. Laszkowicz is a tech editor of this book. Phil's been delivering large-scale software solutions for many years, as well as working with startups as a board member, mentor, and coach. He's worked with neural networks for over a decade, and enjoys combining deep learning with intuitive and elegant user experiences across mobile and web. In his spare time he writes music, drinks coffee at a professional level, and can be found scaling cliff walls, sea kayaking, or taking part in competitive archery. Manda Frederick is an editor of this book. She has been involved in publishing for over ten years through various creative, educational, medical and technical print and digital publications, and is thrilled to bring her experience to the raywenderlich.com family as Managing Editor. In her free time, you can find her at the climbing gym, backpacking in the backcountry, hanging with her dog, working on poems, playing guitar and exploring breweries. Vijay Sharma is the final pass editor of this book. Vijay is a husband, a father and a senior mobile engineer. Based out of Canada's capital, Vijay has worked on dozens of apps for both Android and iOS. When not in front of his laptop, you can find him in front of a TV, behind a book, or chasing after his kids. You can reach out to him on Twitter @vijaysharm or on LinkedIn @vijaysharm raywenderlich.com 5 Machine Learning by Tutorials Machine Learning by Tutorials About the Artist Vicki Wenderlich is the designer and artist of the cover of this book. She is Ray’s wife and business partner. She is a digital artist who creates illustrations, game art and a lot of other art or design work for the tutorials and books on raywenderlich.com. When she’s not making art, she loves hiking, a good glass of wine and attempting to create the perfect cheese plate. raywenderlich.com 6 Machine Learning by Tutorials Table of Contents: Overview Early Access Edition.............................................................. 11 What You Need....................................................................... 12 Book License ............................................................................ 13 Book Source Code & Forums............................................. 14 About the Cover ..................................................................... 15 Chapter 1: Machine Learning, iOS & You...................... 16 Chapter 2: Getting Started with Image Classification............................................................................ 45 Chapter 3: Training the Image Classifier....................... 83 Chapter 4: Getting Started With Python & Turi Create....................................................................................... 109 Chapter 5: Digging Deeper Into Turi Create ............ 147 Chapter 6: Training with Keras ...................................... 174 Chapter 7: Beyond Image Classification .................... 175 Chapter 8: Sequence Classification ............................. 176 Chapter 9: Sequence Predictions.................................. 238 Chapter 10: NLP Classification...................................... 239 Chapter 11: Text-to-Text Transform ............................ 240 Want to Grow Your Skills? ............................................... 241 raywenderlich.com 7 Machine Learning by Tutorials Table of Contents: Extended Early Access Edition.............................................................. 11 What You Need....................................................................... 12 Book License ............................................................................ 13 Book Source Code & Forums............................................. 14 About the Cover ..................................................................... 15 Chapter 1: Machine Learning, iOS & You...................... 16 What is machine learning?............................................................................................. 17 Deep learning...................................................................................................................... 19 ML in a nutshell................................................................................................................... 22 Can mobile devices really do machine learning?................................................... 31 Frameworks, tools and APIs.......................................................................................... 32 ML all the things?............................................................................................................... 39 The ethics of machine learning..................................................................................... 41 Key points............................................................................................................................. 43 Where to go from here?.................................................................................................. 44 Chapter 2: Getting Started with Image Classification............................................................................ 45 Is that snack healthy?....................................................................................................... 46 Core ML................................................................................................................................. 51 Vision...................................................................................................................................... 54 Creating the VNCoreML request................................................................................ 55 Performing the request................................................................................................... 58 Showing the results........................................................................................................... 61 How does it work?............................................................................................................. 67 Multi-class classification................................................................................................. 73 Key points............................................................................................................................. 78 Bonus: Using Core ML without Vision...................................................................... 78 raywenderlich.com 8 Machine Learning by Tutorials Challenges............................................................................................................................ 82 Chapter 3: Training the Image Classifier....................... 83 The dataset........................................................................................................................... 83 Create ML............................................................................................................................. 85 How we created the dataset......................................................................................... 87 Transfer learning................................................................................................................ 89 Logistic regression............................................................................................................. 93 Looking for validation...................................................................................................... 95 More metrics and the test set.................................................................................... 102 Exporting to Core ML.................................................................................................... 105 Recap.................................................................................................................................... 107 Key points........................................................................................................................... 108 Challenge............................................................................................................................ 108 Chapter 4: Getting Started With Python & Turi Create....................................................................................... 109 Starter folder.................................................................................................................... 109 Python................................................................................................................................. 110 Packages and environments....................................................................................... 111 Anaconda............................................................................................................................ 112 Setting up a base ML environment........................................................................... 114 Jupyter notebooks.......................................................................................................... 119 Transfer learning with Turi Create........................................................................... 127 Shutting down Jupyter.................................................................................................. 140 Useful Conda commands............................................................................................. 141 Docker and Colab............................................................................................................ 143 Key points........................................................................................................................... 145 Challenges.......................................................................................................................... 145 Where to go from here?................................................................................................ 146 Chapter 5: Digging Deeper Into Turi Create ............ 147 Getting started................................................................................................................. 147 Transfer learning with SqueezeNet......................................................................... 147 Getting individual predictions................................................................................... 149 raywenderlich.com 9 Machine Learning by Tutorials Increasing max iterations............................................................................................. 154 Confusing apples with oranges?................................................................................ 156 Wrangling Turi Create code........................................................................................ 160 A peek behind the curtain............................................................................................ 169 Key points........................................................................................................................... 170 Challenges.......................................................................................................................... 171 Chapter 6: Training with Keras ...................................... 174 Chapter 7: Beyond Image Classification .................... 175 Chapter 8: Sequence Classification ............................. 176 Building a dataset............................................................................................................ 178 Analyzing and preparing your data.......................................................................... 188 Creating a model............................................................................................................. 199 Getting to know your model....................................................................................... 211 Classifying human activity in your app................................................................... 218 Key points........................................................................................................................... 236 Challenges.......................................................................................................................... 237 Chapter 9: Sequence Predictions.................................. 238 Chapter 10: NLP Classification...................................... 239 Chapter 11: Text-to-Text Transform ............................ 240 Want to Grow Your Skills? ............................................... 241 raywenderlich.com 10

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Most books are stored in the elastic cloud where traffic is expensive. For this reason, we have a limit on daily download.