TensorFlow 2.0 Quick Start Guide (eBook)
196 Seiten
Packt Publishing (Verlag)
978-1-78953-696-6 (ISBN)
Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.
Key Features
- Train your own models for effective prediction, using high-level Keras API
- Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks
- Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha
Book Description
TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks.
After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering.
You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains.
By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.
What you will learn
- Use tf.Keras for fast prototyping, building, and training deep learning neural network models
- Easily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible files
- Use TensorFlow to tackle traditional supervised and unsupervised machine learning applications
- Understand image recognition techniques using TensorFlow
- Perform neural style transfer for image hybridization using a neural network
- Code a recurrent neural network in TensorFlow to perform text-style generation
Who this book is for
Data scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.
Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.Key FeaturesTrain your own models for effective prediction, using high-level Keras API Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networksGet acquainted with some new practices introduced in TensorFlow 2.0 AlphaBook DescriptionTensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks.After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains.By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.What you will learnUse tf.Keras for fast prototyping, building, and training deep learning neural network modelsEasily convert your TensorFlow 1.12 applications to TensorFlow 2.0-compatible filesUse TensorFlow to tackle traditional supervised and unsupervised machine learning applicationsUnderstand image recognition techniques using TensorFlowPerform neural style transfer for image hybridization using a neural networkCode a recurrent neural network in TensorFlow to perform text-style generationWho this book is forData scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will find this book useful. Some Python programming experience with version 3.6 or later, along with a familiarity with Jupyter notebooks will be an added advantage. Exposure to machine learning and neural network techniques would also be helpful.
| Erscheint lt. Verlag | 29.3.2019 |
|---|---|
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | machine learning • Python • supervised • TensorFlow 2.0 • unsupervised |
| ISBN-10 | 1-78953-696-0 / 1789536960 |
| ISBN-13 | 978-1-78953-696-6 / 9781789536966 |
| Haben Sie eine Frage zum Produkt? |
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Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belletristik und Sachbüchern. Der Fließtext wird dynamisch an die Display- und Schriftgröße angepasst. Auch für mobile Lesegeräte ist EPUB daher gut geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür die kostenlose Software Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür eine kostenlose App.
Geräteliste und zusätzliche Hinweise
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
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