| LDR | | 05229cmm u2200517Ma 4500 |
| 001 | | 000000316271 |
| 003 | | OCoLC |
| 005 | | 20230525180111 |
| 006 | | m d |
| 007 | | cr cnu---unuuu |
| 008 | | 190608s2019 xx o 000 0 eng d |
| 019 | |
▼a 1103693554
▼a 1103982098 |
| 020 | |
▼a 1788839269 |
| 020 | |
▼a 9781788839266
▼q (electronic bk.) |
| 035 | |
▼a 2149484
▼b (N$T) |
| 035 | |
▼a (OCoLC)1104086471
▼z (OCoLC)1103693554
▼z (OCoLC)1103982098 |
| 040 | |
▼a EBLCP
▼b eng
▼c EBLCP
▼d YDX
▼d N$T
▼d 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a TA1634 |
| 072 | 7 |
▼a COM
▼x 000000
▼2 bisacsh |
| 082 | 04 |
▼a 006.37
▼2 23 |
| 100 | 1 |
▼a Planche, Benjamin. |
| 245 | 10 |
▼a Hands-On Computer Vision with TensorFlow 2
▼h [electronic resource] :
▼b Leverage Deep Learning to Create Powerful Image Processing Apps with TensorFlow 2. 0 and Keras. |
| 260 | |
▼a Birmingham :
▼b Packt Publishing, Limited,
▼c 2019. |
| 300 | |
▼a 1 online resource (361 p.) |
| 500 | |
▼a Description based upon print version of record. |
| 500 | |
▼a Lack of spatial reasoning |
| 505 | 0 |
▼a 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 |
| 505 | 8 |
▼a 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 |
| 505 | 8 |
▼a 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 |
| 505 | 8 |
▼a 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 |
| 505 | 8 |
▼a 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 |
| 520 | |
▼a 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 a part of the exciting advances in computer vision. |
| 590 | |
▼a Master record variable field(s) change: 050, 072, 082, 630, 650 |
| 630 | 00 |
▼a TensorFlow. |
| 650 | 0 |
▼a Computer vision. |
| 650 | 0 |
▼a Machine learning. |
| 650 | 7 |
▼a COMPUTERS / General.
▼2 bisacsh |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Andres, Eliot. |
| 776 | 08 |
▼i Print version:
▼a Planche, Benjamin
▼t Hands-On Computer Vision with TensorFlow 2 : Leverage Deep Learning to Create Powerful Image Processing Apps with TensorFlow 2. 0 and Keras
▼d Birmingham : Packt Publishing, Limited,c2019
▼z 9781788830645 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2149484 |
| 938 | |
▼a EBL - Ebook Library
▼b EBLB
▼n EBL5783101 |
| 938 | |
▼a YBP Library Services
▼b YANK
▼n 16253192 |
| 938 | |
▼a EBSCOhost
▼b EBSC
▼n 2149484 |
| 990 | |
▼a 관리자 |
| 994 | |
▼a 92
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