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State-of-the-art deep learning model...

Paper, David.

State-of-the-art deep learning models in Tensorflowmodern machine learning in the Google colab ecosystem /

Record Type:	Electronic resources : Monograph/item
Title/Author:	State-of-the-art deep learning models in Tensorflowby David Paper.
Reminder of title:	modern machine learning in the Google colab ecosystem /
Author:	Paper, David.
Published:	Berkeley, CA :Apress :2021.
Description:	xxiv, 374 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-7341-8
ISBN:	9781484273418

State-of-the-art deep learning models in Tensorflowmodern machine learning in the Google colab ecosystem /
Paper, David.

State-of-the-art deep learning models in Tensorflowmodern machine learning in the Google colab ecosystem /[electronic resource] :by David Paper. - Berkeley, CA :Apress :2021. - xxiv, 374 p. :ill., digital ;24 cm.

1. Build TensorFlow Input Pipelines -- 2. Increase the Diversity of your Dataset with Data Augmentation -- 3. TensorFlow Datasets -- 4. Deep Learning with TensorFlow Datasets -- 5. Introduction to Tensor Processing Units -- 6. Simple Transfer Learning with TensorFlow Hub -- 7. Advanced Transfer Learning -- 8. Stacked Autoencoders -- 9. Convolutional and Variational Autoencoders -- 10. Generative Adversarial Networks -- 11. Progressive Growing Generative Adversarial Networks -- 12. Fast Style Transfer -- 13. Object Detection -- 14. An Introduction to Reinforcement Learning.

Use TensorFlow 2.x in the Google Colab ecosystem to create state-of-the-art deep learning models guided by hands-on examples. The Colab ecosystem provides a free cloud service with easy access to on-demand GPU (and TPU) hardware acceleration for fast execution of the models you learn to build. This book teaches you state-of-the-art deep learning models in an applied manner with the only requirement being an Internet connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks. The book begins with an example-driven approach to building input pipelines that feed all machine learning models. You will learn how to provision a workspace on the Colab ecosystem to enable construction of effective input pipelines in a step-by-step manner. From there, you will progress into data augmentation techniques and TensorFlow datasets to gain a deeper understanding of how to work with complex datasets. You will find coverage of Tensor Processing Units (TPUs) and transfer learning followed by state-of-the-art deep learning models, including autoencoders, generative adversarial networks, fast style transfer, object detection, and reinforcement learning. Author Dr. Paper provides all the applied math, programming, and concepts you need to master the content. Examples range from relatively simple to very complex when necessary. Examples are carefully explained, concise, accurate, and complete. Care is taken to walk you through each topic through clear examples written in Python that you can try out and experiment with in the Google Colab ecosystem in the comfort of your own home or office. What You Will Learn Take advantage of the built-in support of the Google Colab ecosystem Work with TensorFlow data sets Create input pipelines to feed state-of-the-art deep learning models Create pipelined state-of-the-art deep learning models with clean and reliable Python code Leverage pre-trained deep learning models to solve complex machine learning tasks Create a simple environment to teach an intelligent agent to make automated decisions.

ISBN: 9781484273418

Standard No.: 10.1007/978-1-4842-7341-8doiSubjects--Uniform Titles:

TensorFlow.
Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .P36 2021

Dewey Class. No.: 006.31

State-of-the-art deep learning models in Tensorflowmodern machine learning in the Google colab ecosystem /
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505 0 $a 1. Build TensorFlow Input Pipelines -- 2. Increase the Diversity of your Dataset with Data Augmentation -- 3. TensorFlow Datasets -- 4. Deep Learning with TensorFlow Datasets -- 5. Introduction to Tensor Processing Units -- 6. Simple Transfer Learning with TensorFlow Hub -- 7. Advanced Transfer Learning -- 8. Stacked Autoencoders -- 9. Convolutional and Variational Autoencoders -- 10. Generative Adversarial Networks -- 11. Progressive Growing Generative Adversarial Networks -- 12. Fast Style Transfer -- 13. Object Detection -- 14. An Introduction to Reinforcement Learning.
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