000			07883cam a2200757Mi 4500
001			on1141023689
003			OCoLC
005			20240523125542.0
006			m o d
007			cr |n|||||||||
008			200220s2020 nju o 000 0 eng d
040			_aYDX _beng _epn _erda _cYDX _dEBLCP _dRECBK _dTEFOD _dOCLCQ _dOCLCO _dYDX _dOCLCF _dN$T _dCOO _dOCLCQ _dUKAHL _dOCLCO _dK6U _dTEF _dOCLCQ _dOH1 _dOCL _dOCLCO _dOCLCL
019			_a1141037410 _a1143846866 _a1147817102 _a1147852120
020			_a9781119602910 _q(electronic bk.)
020			_a1119602912 _q(electronic bk.)
020			_a9781119602903 _q(electronic bk.)
020			_a1119602904 _q(electronic bk.)
020			_a9781119602927 _q(electronic bk.)
020			_a1119602920 _q(electronic bk.)
020			_z1119602874
020			_z9781119602873
024	7		_a10.1002/9781119602927 _2doi
029	1		_aAU@ _b000066881811
029	1		_aAU@ _b000072394053
035			_a(OCoLC)1141023689 _z(OCoLC)1141037410 _z(OCoLC)1143846866 _z(OCoLC)1147817102 _z(OCoLC)1147852120
037			_a3543C3E3-EBE8-4C7B-877D-AE95009596E8 _bOverDrive, Inc. _nhttp://www.overdrive.com
050		4	_aQ325.5 _b.M57 2020
082	0	4	_a006.3/1 _223
049			_aMAIN
100	1		_aMishra, Abhishek, _eauthor.
245	1	0	_aMachine learning for iOS developers / _cAbhishek Mishra.
264		1	_aHoboken, NJ : _bJohn Wiley And Sons, Inc, _c2020.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
505	0		_aIntroduction -- What Does This Book Cover? -- Additional Resources -- Reader Support for This Book -- Part 1 Fundamentals of Machine Learning -- Chapter 1 Introduction to Machine Learning -- What Is Machine Learning? -- Tools Commonly Used by Data Scientists -- Common Terminology -- Real-World Applications of Machine Learning -- Types of Machine Learning Systems -- Supervised Learning -- Unsupervised Learning -- Semisupervised Learning
505	8		_aReinforcement Learning -- Batch Learning -- Incremental Learning -- Instance-Based Learning -- Model-Based Learning -- Common Machine Learning Algorithms -- Linear Regression -- Support Vector Machines -- Logistic Regression -- Decision Trees -- Artificial Neural Networks -- Sources of Machine Learning Datasets -- Scikit-learn Datasets -- AWS Public Datasets -- Kaggle.com Datasets -- UCI Machine Learning Repository -- Summary -- Chapter 2 The Machine-Learning Approach -- The Traditional Rule-Based Approach -- A Machine-Learning System -- Picking Input Features
505	8		_aPreparing the Training and Test Set -- Picking a Machine-Learning Algorithm -- Evaluating Model Performance -- The Machine-Learning Process -- Data Collection and Preprocessing -- Preparation of Training, Test, and Validation Datasets -- Model Building -- Model Evaluation -- Model Tuning -- Model Deployment -- Summary -- Chapter 3 Data Exploration and Preprocessing -- Data Preprocessing Techniques -- Obtaining an Overview of the Data -- Handling Missing Values -- Creating New Features -- Transforming Numeric Features -- One-Hot Encoding Categorical Features -- Selecting Training Features
505	8		_aCorrelation -- Principal Component Analysis -- Recursive Feature Elimination -- Summary -- Chapter 4 Implementing Machine Learning on Mobile Apps -- Device-Based vs. Server-Based Approaches -- Apple's Machine Learning Frameworks and Tools -- Task-Level Frameworks -- Model-Level Frameworks -- Format Converters -- Transfer Learning Tools -- Third-Party Machine-Learning Frameworks and Tools -- Summary -- Part 2 Machine Learning with CoreML, CreateML, and TuriCreate -- Chapter 5 Object Detection Using Pre-trained Models -- What Is Object Detection?
505	8		_aA Brief Introduction to Artificial Neural Networks -- Downloading the ResNet50 Model -- Creating the iOS Project -- Creating the User Interface -- Updating Privacy Settings -- Using the Resnet50 Model in the iOS Project -- Summary -- Chapter 6 Creating an Image Classifier with the Create ML App -- Introduction to the Create ML App -- Creating the Image Classification Model with the Create ML App -- Creating the iOS Project -- Creating the User Interface -- Updating Privacy Settings -- Using the Core ML Model in the iOS Project -- Summary -- Chapter 7 Creating a Tabular Classifier with Create ML
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.
590			_aJohn Wiley and Sons _bWiley Online Library: Complete oBooks
630	0	0	_aiOS (Electronic resource)
630	0	7	_aiOS (Electronic resource) _2fast
650		0	_aMachine learning.
650		0	_aComputers.
650		2	_aComputers
650		2	_aMachine Learning
650		6	_aApprentissage automatique.
650		6	_aOrdinateurs.
650		7	_acomputers. _2aat
650		7	_aCOMPUTERS _xMachine Theory. _2bisacsh
650		7	_aComputers _2fast
650		7	_aMachine learning _2fast
758			_ihas work: _aMachine learning for iOS developers (Text) _1https://id.oclc.org/worldcat/entity/E39PCGVKyQ9XrpRfvxFThGJdQq _4https://id.oclc.org/worldcat/ontology/hasWork
776	0	8	_iPrint version: _aMishra, Abhishek. _tMachine learning for ios developers. _d[Place of publication not identified] : John Wiley And Sons, Inc, 2020 _z1119602874 _z9781119602873 _w(OCoLC)1125970961
856	4	0	_uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119602927
938			_aProQuest Ebook Central _bEBLB _nEBL6109530
938			_aEBSCOhost _bEBSC _n2373480
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