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

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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:
Artificial intelligence.
Mobile computing.
Intelligence artificielle.
Informatique mobile.
artificial intelligence.
Portable & handheld devices: consumer/user guides.
Mobile phones: consumer/user guides.
Natural language & machine translation.
Computers > Natural Language Processing.
Computers > Hardware > Handheld Devices.
Computers > Intelligence (AI) & Semantics.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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