Machine Learning by Tutorials: Beginning Machine Learning for Apple and iOS,2nd Edition
by: Matthijs Hollemans
Released: 2019
Pages: 565
Language 语言: English
Pages: PDF/EPUB
Size: 719 Mb
Book Description
The best book on machine learning for iOS.
Covers CoreML,Vison,image and sequence classifiers,natural language processing,and more.
Get started with Machine Learning for Apple and iOS!
Want to know a secret? Machine learning isn’t really that hard to learn. The truth is,you don’t need a PhD from a prestigious university or a background in mathematics to do machine learning. If you already know how to code,you can pick up machine learning quite easily — promise!
This book will get you started with machine learning on iOS and Apple devices. The first bit is a gentle introduction to the world of machine learning and what it has to offer — as well as what its limitations are. In the rest of the book,you’ll look at each of these topics in more detail,until you know enough to make machine learning a useful tool in your software development toolbox.
There are now several high-level Apple frameworks,including Natural Language,Speech,and Vision,that provide advanced machine learning functionality behind simple APIs as part of Apple’s iOS tooling. Whether you want to convert speech to text,recognize language or grammatical structure,detect faces in photos or track moving objects in video,these frameworks have got you covered.
In this book,you’ll learn how to use these tools and frameworks to make your apps smarter. Even better,you’ll learn how machine learning works behind the scenes — and why this technology is awesome.
This book is for all Apple and iOS developers who are interested in learning how to train models,code image recognition systems,learn how natural language processing works,build sequence classifiers and more.
Machine Learning by Tutorials
About the Cover
What You Need
Book License
Book Source Code & Forums
Section l: Machine Learning with lmages
Chapter 1: Machine Learning,iOS & You
Chapter 2: Getting Started with Image Classification
Chapter 3: Training the Image Classifher
Chapter 4: Getting Started with Python & Turi Create
Chapter 5: Digging Deeper into Turi Create
Chapter 6: Taking Control of Training with Keras
Chapter 7: Going Convolutional
Chapter 8: Advanced Convolutional Neural Networks
Chapter 9: Beyond Classification
Chapter 10: YOLO & Semantic Segmentation
Section ll: Machine Learning with Sequences
Chapter 11: Data Collection for Sequence Classification
Chapter 12: Training a Model for Sequence Classification
Chapter 13: Sequence Classification
Section ll: Natural Language Processing
Chapter 14: Natural Language Classification
Chapter 15: Natural Language Transformation,Part 1
Chapter 16: Natural Language Transformation,Part 2
Conclusion
Photo Credits
