Description |
1 online resource (303 pages) |
Physical Medium |
polychrome |
Description |
text file |
Contents |
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 |
Summary |
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 |
Contents |
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 |
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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 |
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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 |
Note |
Running the training script |
Summary |
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. |
Bibliography |
Includes bibliographical references. |
Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
Subject |
Artificial intelligence.
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Artificial intelligence. |
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Mobile computing.
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Mobile computing. |
Genre/Form |
Electronic books.
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Added Author |
Padmanabhan, Arun.
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Cole, Matt R.
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Other Form: |
Print version: NG, Karthikeyan. Mobile Artificial Intelligence Projects : Develop Seven Projects on Your Smartphone Using Artificial Intelligence and Deep Learning Techniques. Birmingham : Packt Publishing Ltd, ©2019 9781789344073 |
ISBN |
1789347041 |
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9781789347043 (electronic book) |
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