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Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques /

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 Kera...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: NG, Karthikeyan
Otros Autores: Padmanabhan, Arun, Cole, Matt R.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing Ltd, 2019.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Mobile artificial intelligence projects :  |b develop seven projects on your smartphone using artificial intelligence and deep learning techniques /  |c Karthikeyan NG, Arun Padmanabhan, Matt R. Cole. 
260 |a Birmingham :  |b Packt Publishing Ltd,  |c 2019. 
300 |a 1 online resource (303 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
505 0 |a 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 
520 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
500 |a Running the training script 
520 |a 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. 
504 |a Includes bibliographical references. 
588 0 |a Print version record. 
590 |a eBooks on EBSCOhost  |b EBSCO eBook Subscription Academic Collection - Worldwide 
650 0 |a Artificial intelligence. 
650 0 |a Mobile computing. 
650 6 |a Intelligence artificielle. 
650 6 |a Informatique mobile. 
650 7 |a artificial intelligence.  |2 aat 
650 7 |a Portable & handheld devices: consumer/user guides.  |2 bicssc 
650 7 |a Mobile phones: consumer/user guides.  |2 bicssc 
650 7 |a Natural language & machine translation.  |2 bicssc 
650 7 |a Artificial intelligence.  |2 bicssc 
650 7 |a Computers  |x Natural Language Processing.  |2 bisacsh 
650 7 |a Computers  |x Hardware  |x Handheld Devices.  |2 bisacsh 
650 7 |a Computers  |x Intelligence (AI) & Semantics.  |2 bisacsh 
650 7 |a Artificial intelligence.  |2 fast  |0 (OCoLC)fst00817247 
650 7 |a Mobile computing.  |2 fast  |0 (OCoLC)fst01024221 
700 1 |a Padmanabhan, Arun. 
700 1 |a Cole, Matt R. 
776 0 8 |i Print version:  |a NG, Karthikeyan.  |t Mobile Artificial Intelligence Projects : Develop Seven Projects on Your Smartphone Using Artificial Intelligence and Deep Learning Techniques.  |d Birmingham : Packt Publishing Ltd, ©2019  |z 9781789344073 
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