000			03677nam a22002537a 4500
003			OSt
005			20220429051317.0
008			220429b ||||| |||| 00| 0 eng d
020			_a9781119602873
040			_aCvSU-CCAT Campus Library. _bEnglish. _cCvSU-CCAT Campus Library. _erda.
050			_aQ 325.5 _bM57 2020
100			_aMishra, Abhishek, author. _94923
245			_aMachine learning for iOS developers / _cAbhishek Mishra.
260			_a[Place of publication not identified] : _bJohn Wiey & Sons, Inc., _cc2020.
300			_axxi, 327 pages : _billustrations ; _c23 cm
504			_aIncludes index.
505			_aPart 1 : Fundamentals of machine learning Chapter 1 : Introduction to machine learning Chapter 2 : The machine-learning approach Chapter 3 : Data exploration and preprocessing Chapter 4 : Implementing machine learning on mobile apps Part 2 : Machine learning with coreML, CreateML, and TuriCreate Chapter 5 : Object detection using pre-trained models Chapter 6 : Creating an image classifier with the Create ML app Chapter 7 : Creating a tabular classifier with Create ML Chapter 8 : Creating a decision tree classifier Chapter 9 : Creating a logistic regression model using Scikit-learn and Core ML Chapter 10 : Building a deep convolutional neural network with Keras
520			_aHarness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models'both pre-trained and user-built'with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: -Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics -Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming -Develop skills in data acquisition and modeling, classification, and regression.-Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) -Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn' & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps
546			_aIn English text.
650			_aMachine learning. _9301
650			_aComputers. _9414
942			_2lcc _cBK _hQ 325.5 M57 2020 _kCIR
999			_c1634 _d1634