Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python
bLearn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges./b h4Key Features/h4 ulliLearn the fundamentals of Convolutional...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2018
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| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bLearn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges./b h4Key Features/h4 ulliLearn the fundamentals of Convolutional Neural Networks /li liHarness Python and Tensorflow to train CNNs /li liBuild scalable deep learning models that can process millions of items/li/ul h4Book Description/h4 Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. h4What you will learn/h4 ulliTrain machine learning models with TensorFlow /li liCreate systems that can evolve and scale during their life cycle /li liUse CNNs in image recognition and classification /li liUse TensorFlow for building deep learning models /li liTrain popular deep learning models /li liFine-tune a neural network to improve the quality of results with transfer learning /li liBuild TensorFlow models that can scale to large datasets and systems/li/ul h4Who this book is for/h4 This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help |
| Beschreibung: | 1 Online-Ressource (272 Seiten) |
| ISBN: | 9781789132823 |
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| 520 | |a bLearn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges./b h4Key Features/h4 ulliLearn the fundamentals of Convolutional Neural Networks /li liHarness Python and Tensorflow to train CNNs /li liBuild scalable deep learning models that can process millions of items/li/ul h4Book Description/h4 Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. | ||
| 520 | |a This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. | ||
| 520 | |a h4What you will learn/h4 ulliTrain machine learning models with TensorFlow /li liCreate systems that can evolve and scale during their life cycle /li liUse CNNs in image recognition and classification /li liUse TensorFlow for building deep learning models /li liTrain popular deep learning models /li liFine-tune a neural network to improve the quality of results with transfer learning /li liBuild TensorFlow models that can scale to large datasets and systems/li/ul h4Who this book is for/h4 This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help | ||
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| spelling | Zafar, Iffat Verfasser aut Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python Zafar, Iffat 1 Birmingham Packt Publishing Limited 2018 1 Online-Ressource (272 Seiten) txt rdacontent c rdamedia cr rdacarrier bLearn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges./b h4Key Features/h4 ulliLearn the fundamentals of Convolutional Neural Networks /li liHarness Python and Tensorflow to train CNNs /li liBuild scalable deep learning models that can process millions of items/li/ul h4Book Description/h4 Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. h4What you will learn/h4 ulliTrain machine learning models with TensorFlow /li liCreate systems that can evolve and scale during their life cycle /li liUse CNNs in image recognition and classification /li liUse TensorFlow for building deep learning models /li liTrain popular deep learning models /li liFine-tune a neural network to improve the quality of results with transfer learning /li liBuild TensorFlow models that can scale to large datasets and systems/li/ul h4Who this book is for/h4 This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help COMPUTERS / Neural Networks COMPUTERS / Data Modeling & Design Tzanidou, Giounona Sonstige oth Burton, Richard Sonstige oth Patel, Nimesh Sonstige oth |
| spellingShingle | Zafar, Iffat Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python COMPUTERS / Neural Networks COMPUTERS / Data Modeling & Design |
| title | Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python |
| title_auth | Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python |
| title_exact_search | Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python |
| title_exact_search_txtP | Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python |
| title_full | Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python Zafar, Iffat |
| title_fullStr | Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python Zafar, Iffat |
| title_full_unstemmed | Hands-On Convolutional Neural Networks with TensorFlow Solve computer vision problems with modeling in TensorFlow and Python Zafar, Iffat |
| title_short | Hands-On Convolutional Neural Networks with TensorFlow |
| title_sort | hands on convolutional neural networks with tensorflow solve computer vision problems with modeling in tensorflow and python |
| title_sub | Solve computer vision problems with modeling in TensorFlow and Python |
| topic | COMPUTERS / Neural Networks COMPUTERS / Data Modeling & Design |
| topic_facet | COMPUTERS / Neural Networks COMPUTERS / Data Modeling & Design |
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