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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | Running the training script |
| Beschreibung: | 1 online resource (303 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 1789347041 9781789347043 |
Internformat
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| 100 | 1 | |a NG, Karthikeyan. | |
| 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 | ||
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| 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. | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 0 | |a Mobile computing. |0 http://id.loc.gov/authorities/subjects/sh95004596 | |
| 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 | |
| 650 | 7 | |a Mobile computing |2 fast | |
| 655 | 4 | |a Electronic book. | |
| 700 | 1 | |a Padmanabhan, Arun. | |
| 700 | 1 | |a Cole, Matt R. | |
| 758 | |i has work: |a Mobile artificial intelligence projects (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGBMFPg3GKwxkkGWFGGrMd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 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 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2094769 |3 Volltext |
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| 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 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 |
| ctrlnum | (OCoLC)1096524450 |
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| discipline | Informatik |
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| spelling | NG, Karthikeyan. Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques / Karthikeyan NG, Arun Padmanabhan, Matt R. Cole. Birmingham : Packt Publishing Ltd, 2019. 1 online resource (303 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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 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 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 Running the training script 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. Includes bibliographical references. Print version record. Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Mobile computing. http://id.loc.gov/authorities/subjects/sh95004596 Intelligence artificielle. Informatique mobile. artificial intelligence. aat Portable & handheld devices: consumer/user guides. bicssc Mobile phones: consumer/user guides. bicssc Natural language & machine translation. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Hardware Handheld Devices. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Mobile computing fast Electronic book. Padmanabhan, Arun. Cole, Matt R. has work: Mobile artificial intelligence projects (Text) https://id.oclc.org/worldcat/entity/E39PCGBMFPg3GKwxkkGWFGGrMd https://id.oclc.org/worldcat/ontology/hasWork 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 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2094769 Volltext |
| spellingShingle | NG, Karthikeyan Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques / 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 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Mobile computing. http://id.loc.gov/authorities/subjects/sh95004596 Intelligence artificielle. Informatique mobile. artificial intelligence. aat Portable & handheld devices: consumer/user guides. bicssc Mobile phones: consumer/user guides. bicssc Natural language & machine translation. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Hardware Handheld Devices. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Mobile computing fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh95004596 |
| title | Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques / |
| title_auth | Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques / |
| title_exact_search | Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques / |
| title_full | Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques / Karthikeyan NG, Arun Padmanabhan, Matt R. Cole. |
| title_fullStr | Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques / Karthikeyan NG, Arun Padmanabhan, Matt R. Cole. |
| title_full_unstemmed | Mobile artificial intelligence projects : develop seven projects on your smartphone using artificial intelligence and deep learning techniques / Karthikeyan NG, Arun Padmanabhan, Matt R. Cole. |
| title_short | Mobile artificial intelligence projects : |
| title_sort | mobile artificial intelligence projects develop seven projects on your smartphone using artificial intelligence and deep learning techniques |
| title_sub | develop seven projects on your smartphone using artificial intelligence and deep learning techniques / |
| topic | Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Mobile computing. http://id.loc.gov/authorities/subjects/sh95004596 Intelligence artificielle. Informatique mobile. artificial intelligence. aat Portable & handheld devices: consumer/user guides. bicssc Mobile phones: consumer/user guides. bicssc Natural language & machine translation. bicssc Artificial intelligence. bicssc Computers Natural Language Processing. bisacsh Computers Hardware Handheld Devices. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Artificial intelligence fast Mobile computing fast |
| topic_facet | 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. Artificial intelligence Mobile computing Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2094769 |
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