Index

A

• accuracy / Evaluation of the model
• agent / Reinforcement learning
• algorithms
  • about / Introduction to algorithms
  • decision tree / Decision tree 
  • random forests / Random forests
• Android app
  • creating / Creating the Android app
  • TF Model, copying / Copying the TF Model
  • activity, creating / Creating an activity
• android application, creating with fritz pre-built models
  • about / Creating an android application using fritz pre-built models
  • dependencies, adding / Adding dependencies to the project
  • Fritz JobService, registering / Registering the Fritz JobService in your Android Manifest
  • app layout and components, creating / Creating the app layout and components
  • application, coding / Coding the application
• Artificial Intelligence (AI)
  • about / Where is data science used?, What is AI?
  • general AI / What is AI?
  • narrow AI / What is AI?
  • relationship, with data science / How are data science, AI, and machine learning interrelated?
• artificial neural networks (ANNs) / What are artificial neural networks?
• assignment problem statement / Image recognition solution
• association-rule learning algorithm / Association rule learning algorithm

B

• batch prediction / Core ML
• bias / Evaluation of the model
• big data
  • volume / What is big data?
  • velocity / What is big data?
  • variety / What is big data?
• Bluetooth Low Energy (BLE) / E-commerce 
• Boston
  • dataset naming / Dataset naming
• Breast Cancer dataset
  • about / Dataset
  • naming / Naming the dataset

C

• Caffe2
  • reference / Caffe2
• Carat / Carat
• Classification and Regression Trees (CART) / Decision trees
• classification tree / Decision tree 
• clustering algorithms
  • similarity function / Clustering algorithms
• clustering methods
  • about / Clustering methods
  • hierarchical agglomerative / Hierarchical agglomerative clustering methods
  • K-means clustering / K-means clustering
• confusion matrix / Evaluation of the model
• Conv Nets
  • reference / Retraining the model
• convolutional neural networks (CNNs) / Understanding the model concepts
• Core ML
  • basics / Understanding the basics of Core ML
  • problem solving, with linear SVM / Solving the problem using linear SVM in Core ML
• Core ML model, using with Fritz
  • about / Using the existing Core ML model in an iOS application using Fritz
  • account, signing up / Registering with Fritz
  • account, logging in / Creating a new project in Fritz
  • model file (.pb or .tflite), uploading / Uploading the model file (.pb or .tflite)
  • Xcode project, creating / Creating an Xcode project
  • code, adding / Adding code
  • iOS mobile application, building / Building and running the iOS mobile application
  • iOS mobile application, executing / Building and running the iOS mobile application
• cross-validation / Evaluation of the model

D

• Dango / Dango
• data mining / What is data mining?
• data science
  • about / Data science, What is data science?
  • using / Where is data science used?
  • and big data, relationship / Relationship between data science and big data
• decision trees
  • internal node / Decision trees
  • edges / Decision trees
  • leaf / Decision trees
  • about / Decision tree 
  • advantages / Advantages of the decision tree algorithm, Advantages of decision trees
  • purity parameter / Advantages of the decision tree algorithm
  • disadvantages / Disadvantages of decision trees
• directed acyclic graphs (DAGs) / Decision tree 

E

• error / Evaluation of the model
• error matrix / Evaluation of the model

F

• Facebook / Facebook
• face detection
  • concepts / Face detection concepts
  • solution, obtaining with ML Kit / Sample solution for face detection using ML Kit
• face orientation / Face detection concepts
• face recognition / Face detection concepts
• face tracking / Face detection concepts
• feature engineering
  • about / Introducing NLP, Feature engineering
  • entity extraction / Entity extraction
  • topic modeling / Topic modeling
  • bag-of-words model / Bag-of-words model
  • Statistical Engineering / Statistical Engineering
  • TF-IDF / TF–IDF, TF-IDF
  • TF / TF
  • Inverse Document Frequency (IDF) / Inverse Document Frequency (IDF)
• features, Google Cloud Vision
  • label detection / Features of Google Cloud Vision
  • image attribute detection / Features of Google Cloud Vision
  • face detection / Features of Google Cloud Vision
  • logo detection / Features of Google Cloud Vision
  • landmark detection / Features of Google Cloud Vision
  • optical character recognition / Features of Google Cloud Vision
  • Explicit Content Detection / Features of Google Cloud Vision
  • Search Web / Features of Google Cloud Vision
• fine needle aspirate (FNA) / Dataset
• Firebase on-cloud APIs
  • used, for creating text recognition app / Creating a text recognition app using Firebase on-cloud APIs
• Firebase on-device APIs
  • used, for creating text recognition app / Creating a text recognition app using Firebase on-device APIs
  • reference / Creating a text recognition app using Firebase on-device APIs
• forecasting problem / Introduction to regression
• Fritz
  • about / Introduction to Fritz
  • prebuilt ML models / Prebuilt ML models
  • custom models, using / Ability to use custom models
  • model management / Model management
  • using, examples / Hand-on samples using Fritz
  • existing TensorFlow for mobile model, using / Using the existing TensorFlow for mobile model in an Android application using Fritz
  • registering with / Registering with Fritz, Setting up Android and registering the app
  • model file (.pb or .tflite), uploading / Uploading the model file (.pb or .tflite)
  • setting up, in Android / Setting up Android and registering the app
  • TFMobile library, adding / Adding Fritz's TFMobile library
  • dependencies, adding / Adding dependencies to the project
  • building / Building and running the application
  • executing / Building and running the application
  • new version of model, deploying / Deploying a new version of your model
  • using, with Core ML model / Using the existing Core ML model in an iOS application using Fritz
• Fritz Interpreter
  • TensorFlowInferenceInterface class, replacing with / Replacing the TensorFlowInferenceInterface class with Fritz Interpreter
• FritzJob service
  • registering, in Android Manifest / Registering the FritzJob service in your Android Manifest

G

• GBoard / GBoard
• Google Cloud Vision
  • features / Features of Google Cloud Vision
  • reference / Features of Google Cloud Vision
  • used, for creating mobile application / Sample mobile application using Google Cloud Vision
• Google Maps / Google Maps

H

• handwritten digit-recognition problem
  • solution / Handwritten digit recognition solution, Problem solution
• hyperplane / Introduction to regression

I

• IKEA Pace
  • reference / Real estate
• ImprompDo / ImprompDo
• indoor navigation / E-commerce 
• innovation areas
  • about / Key innovation areas
  • personalization applications / Personalization applications
  • healthcare / Healthcare
  • targeted promotions and marketing / Targeted promotions and marketing
  • visual and audio recognition / Visual and audio recognition
  • e-commerce / E-commerce 
  • finance management / Finance management
  • gaming and entertainment / Gaming and entertainment
  • enterprise apps / Enterprise apps
  • real estate / Real estate
  • agriculture / Agriculture
  • energy / Energy
  • mobile security / Mobile security
• installations
  • about / Installation
  • Python / Python
  • Python dependencies / Python dependencies
  • Xcode / Xcode
• Inverse Document Frequency (IDF) / Inverse Document Frequency (IDF)
• iOS Mobile application
  • writing / Writing the iOS mobile application

K

• Keras
  • about / Introduction to Keras
  • uses / Introduction to Keras
  • reference / Introduction to Keras
  • installing / Installing Keras
• Keras model
  • building, with sequential API / Defining the model's architecture
• Kernel Trick / Understanding linear SVM algorithm

L

• landmark / Face detection concepts
• learning
  • types / Types of learning
  • supervised learning / Supervised learning
  • unsupervised learning / Unsupervised learning
  • semi-supervised learning / Semi-supervised learning
  • reinforcement learning / Reinforcement learning
  • challenges / Challenges in machine learning
• linear regression
  • about / Linear regression, Linear regression
  • dataset / Dataset
  • dataset naming / Dataset naming
• Linear SVM algorithm
  • about / Understanding linear SVM algorithm
  • used, for problem solving in Core ML / Solving the problem using linear SVM in Core ML
• logistic regression / Logistic regression

M

• machine learning
  • defining / Definition of machine learning
  • using, scenarios / When is it appropriate to go for machine learning systems?
  • process / The machine learning process
  • issue, defining / Defining the machine learning problem
  • model, building / Building the model
  • predictions, deploying / Making predictions/Deploying in the field
  • using, on mobile devices / Why use machine learning on mobile devices?
  • on mobile, advantages / Why use machine learning on mobile devices?
  • implementing, in mobile application / Ways to implement machine learning in mobile applications
  • service providers, utilizing / Utilizing machine learning service providers for a machine learning model
  • model, training / Ways to train the machine learning model
  • training, on desktop / On a desktop (training in the cloud)
  • training, on device / On a device
  • inference process, on server / Ways to carry out the inference – making predictions
  • process, on device / Ways to carry out the inference – making predictions, Inference on a device
  • process, on server / Inference on a server
  • mobile tools / Popular mobile machine learning tools and SDKs
  • SDKs / Popular mobile machine learning tools and SDKs
  • relationship, with data science / How are data science, AI, and machine learning interrelated?
• machine learning framework
  • about / Machine learning framework 
  • Caffe2 / Caffe2
  • scikit-learn / scikit-learn
  • TensorFlow / TensorFlow
  • Core ML / Core ML
• market-basket analysis / Association rule learning algorithm
• maximum-margin classifier / Support vector machines
• maximum-margin hyperplane / Support vector machines
• ML Kit
  • about / Understanding ML Kit
  • machine learning scenarios / Understanding ML Kit
  • APIs / ML Kit APIs
  • used, for face detection / Face detection using ML Kit
  • used, for finding solution for face detection / Sample solution for face detection using ML Kit
  • app, executing / Running the app
• ML Kit APIs
  • about / ML Kit APIs
  • text recognition / Text recognition
  • face detection / Face detection
  • barcode scanning / Barcode scanning
  • image labeling / Image labeling
  • landmark recognition / Landmark recognition
  • custom model inference / Custom model inference
• ML mobile applications
  • about / Key ML mobile applications 
  • Facebook / Facebook
  • Google Maps / Google Maps
  • Snapchat / Snapchat
  • Tinder / Tinder
  • Netflix / Netflix
  • Oval Money / Oval Money
  • ImprompDo / ImprompDo
  • Dango / Dango
  • Carat / Carat
  • Uber / Uber
  • GBoard / GBoard
• MNIST
  • reference / Defining the problem statement
• mobile application, creating with Google Cloud Vision
  • label detection, working / How does label detection work?
  • prerequisites / Prerequisites
  • key activities / Preparations
  • working / Understanding the Application
  • output / Output
• mobile machine learning application
  • architecture / The architecture of a mobile machine learning application
  • model concepts / Understanding the model concepts
• mobile machine learning project implementation
  • about / Mobile machine learning project implementation
  • high-level important items, considering / What are the high-level important items to be considered before starting the project?
  • skills required / What are the roles and skills required to implement a mobile machine learning project?
  • testing /  What should you focus on when testing the mobile machine learning project?
  • domain expert help / What is the help that the domain expert will provide to the machine learning project?
  • pitfalls / What are the common pitfalls in machine learning projects?
• model building phase
  • about / Building the model
  • right machine learning algorithm, selecting / Selecting the right machine learning algorithm
  • machine learning model, training / Training the machine learning model
  • testing / Testing the model
  • evaluating / Evaluation of the model
• multivariate regression problem / Introduction to regression

N

• Naive Bayes / Naive Bayes
• named entity recognition (NER) / Entity extraction
• natural language processing (NLP)
  • about / Understanding NLP, Introducing NLP
  • semantic information / Introducing NLP
  • syntactic information / Introducing NLP
  • pragmatic information (context) / Introducing NLP
  • text, classifying/clustering / Classifying/clustering the text
• Netflix / Netflix
• neural networks
  • about / Introduction to neural networks
  • neuron, communications / Communication steps of  a neuron
  • activation function / The activation function
  • neurons, arranging / Arrangement of neurons
  • types / Types of neural networks
  • neural networks / Types of neural networks
  • CNN / Types of neural networks
  • Recurrent Neural Networks / Types of neural networks
  • implementation / Solving the problem
  • handwritten digits recognition problem statement, defining / Defining the problem statement
• NLP processing / Introducing NLP

O

• on-device machine learning
  • implementation, skills / Skills needed to implement on-device machine learning
• opportunities, for stakeholders
  • hardware manufacturers / Hardware manufacturers
  • mobile operating system vendors / Mobile operating system vendors
  • third-party mobile ML SDK providers / Third-party mobile ML SDK providers
  • ML mobile application developers / ML mobile application developers
• Oval Money / Oval Money
• overfitting / Evaluation of the model

P

• pandas
  • reference / Creating the model file using scikit-learn 
• plane / Introduction to regression
• prebuilt ML models, Fritz
  • object detection / Prebuilt ML models
  • image labeling / Prebuilt ML models
• precision / Evaluation of the model
• preprocessing / Introducing NLP
• Principal component analysis (PCA)
  • reference / Deep dive into unsupervised learning algorithms
• Python
  • reference / Python

Q

• quantization / Core ML

R

• random forest, Core ML
  • used, for problem solving / Solving the problem using random forest in Core ML
  • Breast Cancer dataset / Dataset
  • requisites / Technical requirements
  • model file, creating with scikit-learn / Creating the model file using scikit-learn 
  • scikit model, converting / Converting the scikit model to the Core ML model
  • iOS mobile application, creating / Creating an iOS mobile application using the Core ML model
• random forests
  • about / Random forest, Random forests
  • applying, areas / Random forest
  • comparing, with decision trees / Random forests
• recall / Evaluation of the model
• references / References 
• refinements, face detection
  • landmark detection / Face detection
  • classification / Face detection
• regression analysis / Introduction to regression
• regression model
  • used, for problem solving / Solving the problem using regression in Core ML
  • creating, requisites / Technical requirements
  • creating, with scikit-learn / How to create the model file using scikit-learn
  • about / How to create the model file using scikit-learn
  • testing / Running and testing the model
  • executing / Running and testing the model
  • importing, to iOS project / Importing the model into the iOS project
  • iOS application, writing / Writing the iOS application
  • iOS application, executing / Running the iOS application
• regression trees / Decision trees
• reinforcement learning / Reinforcement learning
• reinforcement signal / Reinforcement learning
• root mean squared error (RMSE) / How to create the model file using scikit-learn

S

• semi-supervised learning / Semi-supervised learning
• singular value decomposition (SVD)
  • reference / Deep dive into unsupervised learning algorithms
• Snapchat / Snapchat
• soft margin / Support vector machines
• solution, handwritten digits recognition problem
  • data, preparing / Preparing the data
  • model's architecture, defining / Defining the model's architecture
  • model, fitting / Compiling and fitting the model
  • model, compiling / Compiling and fitting the model
  • Keras model, converting to CoreML model / Converting the Keras model into the Core ML model
  • iOS mobile application, creating / Creating the iOS mobile application
• spam message-detection problem
  • solving / Solving the problem using linear SVM in Core ML
  • data / About the data
  • prerequisites / Technical requirements
  • model file, creating with Scikit Learn / Creating the Model file using Scikit Learn 
  • Scikit-learn model, converting / Converting the scikit-learn model into the Core ML model
  • iOS application, writing / Writing the iOS application
• stakeholders
  • opportunities / Opportunities for stakeholders
• supervised learning
  • about / Supervised learning, Deep dive into supervised learning algorithms
  • classification problems / Deep dive into supervised learning algorithms
  • regression problems / Deep dive into supervised learning algorithms
• supervised learning algorithms
  • about / Introduction to supervised learning algorithms
  • steps / Introduction to supervised learning algorithms
  • exploring / Deep dive into supervised learning algorithms
  • Naive Bayes / Naive Bayes
  • decision trees / Decision trees
  • linear regression / Linear regression
  • logistic regression / Logistic regression
  • support vector machine (SVM) / Support vector machines
  • random forest / Random forest
• support vector classifier / Support vector machines
• support vector machine (SVM) / Support vector machines

T

• TensorFlow / An introduction to TensorFlow
• TensorFlow image-recognition model
  • creating / Creating a TensorFlow image recognition model
  • retraining / Retraining the model
  • bottlenecks / About bottlenecks
• TensorFlow Lite
  • comparing, with TensorFlow for mobile / An introduction to TensorFlow
  • components / TensorFlow Lite components
  • Inception V3 / Interface to hardware acceleration
  • MobileNets / Interface to hardware acceleration
  • on-device smart reply / Interface to hardware acceleration
  • reference / Writing the mobile application using the TensorFlow model
• TensorFlow Lite components
  • about / TensorFlow Lite components
  • model-file format / Model-file format
  • interpreter / Interpreter
  • Ops/Kernel / Ops/Kernel
  • interface to hardware acceleration / Interface to hardware acceleration
• TensorFlow mobile application
  • writing, with TransferFlow model / Writing the mobile application using the TensorFlow model
  • first program, writing / Writing our first program
  • Android app, creating / Creating the Android app
• TensorFlow model
  • used, for writing mobile application / Writing the mobile application using the TensorFlow model
  • creating / Creating and Saving the TF model
  • saving / Creating and Saving the TF model
  • graph, freezing / Freezing the graph
  • file, optimizing / Optimizing the model file
  • converting, to CoreML Model / Converting the TensorFlow model into the Core ML model
  • iOS Mobile application, writing / Writing the iOS mobile application
• tensor processing units (TPUs) / Creating a TensorFlow image recognition model
• Term Frequency-Inverse Document Frequency (TF-IDF) / Statistical Engineering
• text-preprocessing techniques
  • about / Text-preprocessing techniques
  • Noise, removing / Removing noise
  • normalization / Normalization
  • standardization / Standardization
• text recognition (OCR) model
  • used, for creating text recognition app / Creating a text recognition app using Firebase on-device APIs
• text recognition app
  • creating, with Firebase on-device APIs / Creating a text recognition app using Firebase on-device APIs, Creating a text recognition app using Firebase on-cloud APIs
  • creating, with Firebase on-cloud APIs / Creating a text recognition app using Firebase on-cloud APIs
• Tinder / Tinder
• training data / Deep dive into supervised learning algorithms

U

• Uber / Uber
• underfitting / Evaluation of the model
• unsupervised learning
  • used, for pattern detection / Unsupervised learning
  • used, for descriptive modeling / Unsupervised learning
  • Testing Phase / Unsupervised learning
  • algorithms / Unsupervised learning
• unsupervised learning algorithms
  • about / Introduction to unsupervised learning algorithms
  • exploring / Deep dive into unsupervised learning algorithms
  • clustering algorithms / Clustering algorithms
  • clustering methods / Clustering methods
  • association-rule learning algorithm / Association rule learning algorithm

V

• variance / Evaluation of the model