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|a ABES
|b eng
|e AFNOR
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|a eng
|2 639-2
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| 100 |
1 |
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|a NG, Karthikeyan.
|4 aut.
|e Auteur
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| 245 |
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|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.
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| 264 |
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|a Birmingham :
|b Packt Publishing,
|c 2019.
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| 300 |
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|a 1 vol. (303 p.)
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| 336 |
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|b txt
|2 rdacontent
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|2 rdamedia
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|a Numérisation de l'édition de Packt Publishing.
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| 500 |
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|a La pagination de l'édition imprimée correspondante est de 303 p.
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|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 -- 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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|a L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition
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|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
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|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
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|a Configuration requise : navigateur internet.
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|a Padmanabhan, Arun.
|4 aut.
|e Auteur
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|a Cole, Matt R..
|4 aut.
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|q HTML
|u https://srvext.uco.fr/login?url=https://univ.scholarvox.com/book/88867573
|w Données éditeur
|z Accès à l'E-book
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