Machine Learning with Core ML: An iOS developer's guide to implementing machine learning in mobile apps - Image 1

£41.99

Packt Publishing Machine Learning with Core ML: An iOS developer's guide to implementing machine learning in mobile apps

Name: Machine Learning with Core ML: An iOS developer's guide to implementing machine learning in mobile apps
Brand: Packt Publishing
Price: 41.99 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 40 days • 40 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 40 days • 1 price levels

Days at Price

Price Analysis

Most common price: £42 (40 days, 100.0%)

Price range: £42 - £42

Price levels: 1 different prices over 40 days

Description

Leverage the power of Apple's Core ML to create smart iOS apps Key Features Explore the concepts of machine learning and Apple's Core ML APIs Use Core ML to understand and transform images and videos Exploit the power of using CNN and RNN in iOS applications Book Description Core ML is a popular framework by Apple, with APIs designed to support various machine learning tasks. It allows you to train your machine learning models and then integrate them into your iOS apps. Machine Learning with Core ML is a fun and practical guide that not only demystifies Core ML but also sheds light on machine learning. In this book, you'll walk through realistic and interesting examples of machine learning in the context of mobile platforms (specifically iOS). You'll learn to implement Core ML for visual-based applications using the principles of transfer learning and neural networks. Having got to grips with the basics, you'll discover a series of seven examples, each providing a new use-case that uncovers how machine learning can be applied along with the related concepts. By the end of the book, you will have the skills required to put machine learning to work in their own applications, using the Core ML APIs What you will learn Understand components of an ML project using algorithms, problems, and data Master Core ML by obtaining and importing machine learning model, and generate classes Prepare data for machine learning model and interpret results for optimized solutions Create and optimize custom layers for unsupported layers Apply CoreML to image and video data using CNN Learn the qualities of RNN to recognize sketches, and augment drawing Use Core ML transfer learning to execute style transfer on images Who This Book Is For Machine Learning with Core ML is for you if you are an intermediate iOS developer interested in applying machine learning to your mobile apps. This book is also for those who are machine learning developers or deep learning practitioners who want to bring the power of neural networks in their iOS apps. Some exposure to machine learning concepts would be beneficial but not essential, as this book acts as a launchpad into the world of machine learning for developers. Table of Contents Introduction to Machine Learning Introduction to Apple Core ML Recognising objects in the world Locating Objects in the World Facial Emotion Detection with Convolutional Neural Networks Transfer Learning - Creating art with style transfer Assisted drawing with Convolutional Neural Networks Assisted drawing with Recurrent Neural Networks Object segmentation using neural networks An introduction to Create ML

Product Specifications

Brand: Packt Publishing
Format: paperback
ASIN: 1788838297
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 28 June 2018
Listed Since: 29 May 2018

Barcode

No barcode data available

Packt Publishing Machine Learning with Core ML: An iOS developer's guide to implementing machine learning in mobile apps

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Machine Learning by Tutorials (Second Edition): Beginning Machine Learning for Apple and iOS

Develop Intelligent iOS Apps with Swift: Understand Texts, Classify Sentiments, and Autodetect Answers in Text Using NLP

Intelligent Mobile Projects with TensorFlow: Build 10+ Artificial Intelligence apps using TensorFlow Mobile and Lite for iOS, Android, and Raspberry Pi

Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

Pro iPhone Development with SwiftUI: Design and Manage Top-Quality Apps

Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities

MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities

MATLAB for Machine Learning: Practical examples of regression, clustering and neural networks

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Machine Learning and its Applications

Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

.NET MAUI Projects: Build multi-platform desktop and mobile apps from scratch using C# and Visual Studio 2022

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Practical Deep Learning for Computer Vision with Python

Codeless Deep Learning with KNIME: Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques

scikit-learn : Machine Learning Simplified: Implement scikit-learn into every step of the data science pipeline

Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

Practical Machine Learning with Python: A Problem-Solver's Guide to Building Real-World Intelligent Systems