TY  - BOOK
AU  - NG,Karthikeyan
AU  - Padmanabhan,Arun
AU  - Cole,Matt R.
TI  - Mobile artificial intelligence projects: develop seven projects on your smartphone using artificial intelligence and deep learning techniques 
SN  - 1789347041
AV  - QA76.59 
U1  - 004.165 23
PY  - 2019///
CY  - Birmingham
PB  - Packt Publishing Ltd
KW  - Artificial intelligence
KW  - Mobile computing
KW  - Intelligence artificielle
KW  - Informatique mobile
KW  - artificial intelligence
KW  - aat
KW  - Portable & handheld devices: consumer/user guides
KW  - bicssc
KW  - Mobile phones: consumer/user guides
KW  - Natural language & machine translation
KW  - Computers
KW  - Natural Language Processing
KW  - bisacsh
KW  - Hardware
KW  - Handheld Devices
KW  - Intelligence (AI) & Semantics
KW  - fast
KW  - Electronic books
N1  - Running the training script; Includes bibliographical references; Intro; Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Artificial Intelligence Concepts and Fundamentals; AI versus machine learning versus deep learning; Evolution of AI; The mechanics behind ANNs; Biological neurons; Working of artificial neurons; Scenario 1; Scenario 2; Scenario 3; ANNs; Activation functions; Sigmoid function; Tanh function; ReLU function ; Cost functions; Mean squared error; Cross entropy; Gradient descent; Backpropagation -- a method for neural networks to learn; Softmax; TensorFlow Playground; Creating a new Android project with a single screenDesigning the layout of the screen; Adding a functionality to accept input; Adding a functionality to consume the RESTful API that serves the model; Additional notes; Creating an iOS app to predict house prices; Downloading and installing Xcode; Creating a new iOS project with a single screen; Designing the layout of the screen; Adding a functionality to accept input; Adding a functionality to consume the RESTful API that serves the model; Additional notes; Summary; Chapter 3: Implementing Deep Net Architectures to Recognize Handwritten DigitsBuilding a feedforward neural network to recognize handwritten digits, version one; Building a feedforward neural network to recognize handwritten digits, version two; Building a deeper neural network; Introduction to Computer Vision; Machine learning for Computer Vision; Conferences help on Computer Vision; Summary; Further reading; Chapter 4: Building a Machine Vision Mobile App to Classify Flower Species; CoreML versus TensorFlow Lite; CoreML; TensorFlow Lite; What is MobileNet?; Datasets for image classification; Creating your own image dataset using Google imagesAlternate approach of creating custom datasets from videos; Building your model using TensorFlow; Running TensorBoard; Summary; Chapter 5: Building an ML Model to Predict Car Damage Using TensorFlow; Transfer learning basics; Approaches to transfer learning; Building the TensorFlow model; Installing TensorFlow; Training the images; Building our own model; Retraining with our own images; Architecture; Distortions; Hyperparameters; Image dataset collection; Introduction to Beautiful Soup; Examples; Dataset preparation
N2  - Further reading; Chapter 2: Creating a Real-Estate Price Prediction Mobile App; Setting up the artificial intelligence environment ; Downloading and installing Anaconda; Advantages of Anaconda; Creating an Anaconda environment; Installing dependencies; Building an ANN model for prediction using Keras and TensorFlow; Serving the model as an API; Building a simple API to add two numbers; Building an API to predict the real estate price using the saved model; Creating an Android app to predict house prices; Downloading and installing Android Studio; Artificial intelligence (AI) is rapidly becoming the most popular topic in business and science. This book introduces AI concepts and their use cases with a hands-on and application-focused approach. We will cover a range of projects covering tasks such as automated reasoning, facial recognition, digital assistants, auto text generation, and more
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ER  -