Hands-On Computer Vision with TensorFlow 2 : Leverage Deep Learning to Create Powerful Image Processing Apps with TensorFlow 2. 0 and Keras.

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Computer vision is achieving a new frontier of capabilities in fields like health, automobile or robotics. This book explores TensorFlow 2, Google's open-source AI framework, and teaches how to leverage deep neural networks for visual tasks. It will help you acquire the insight and skills to be...

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Planche, Benjamin
Other Authors: Andres, Eliot
Format: Electronic eBook
Language:Inglés
Published: Birmingham : Packt Publishing, Limited, 2019.
Subjects:
TensorFlow.
Computer vision.
Machine learning.
Vision par ordinateur.
Apprentissage automatique.
COMPUTERS > General.
Computer vision
Machine learning
Online Access:Texto completo
Table of Contents:
  • Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision; Chapter 1: Computer Vision and Neural Networks; Technical requirements; Computer vision in the wild; Introducing computer vision; Main tasks and their applications; Content recognition; Object classification; Object identification; Object detection and localization; Object and instance segmentation; Pose estimation; Video analysis; Instance tracking; Action recognition; Motion estimation; Content-aware image edition
  • Scene reconstructionA brief history of computer vision; First steps to first successes; Underestimating the perception task; Hand-crafting local features; Adding some machine learning on top; Rise of deep learning; Early attempts and failures; Rise and fall of the perceptron; Too heavy to scale; Reasons for a comeback; The internet
  • the new El Dorado of data science; More power than ever; Deep learning or the rebranding of artificial neural networks; What makes learning deep?; Deep learning era; Getting started with neural networks; Building a neural network; Imitating neurons
  • Biological inspirationMathematical model; Implementation; Layering neurons together; Mathematical model; Implementation; Applying our network to classification; Setting up the task; Implementing the network; Training a neural network; Learning strategies; Supervised learning; Unsupervised learning; Reinforcement learning; Teaching time; Evaluating the loss; Back-propagating the loss; Teaching our network to classify; Training considerations
  • underfitting and overfitting; Summary; Questions; Further reading; Chapter 2: TensorFlow Basics and Training a Model; Technical requirements
  • Getting started with TensorFlow 2 and KerasIntroducing TensorFlow; TensorFlow main architecture; Introducing Keras; A simple computer vision model using Keras; Preparing the data; Building the model; Training the model; Model performance; TensorFlow 2 and Keras in detail; Core concepts; Introducing tensors; TensorFlow graph; Comparing lazy execution to eager execution; Creating graphs in TensorFlow 2; Introducing TensorFlow AutoGraph and tf.function; Backpropagating error using the gradient tape; Keras models and layers; Sequential and Functional APIs; Callbacks; Advanced concepts
  • How tf.function worksVariables in TensorFlow 2; Distribute strategies; Using the Estimator API; Available pre-made Estimators; Training a custom Estimator; TensorFlow ecosystem; TensorBoard; TensorFlow Addons and TensorFlow Extended; TensorFlow Lite and TensorFlow.js; Where to run your model; On a local machine; On a remote machine; On Google Cloud; Summary; Questions; Chapter 3: Modern Neural Networks; Technical requirements; Discovering convolutional neural networks; Neural networks for multidimensional data; Problems with fully-connected networks; Explosive number of parameters

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