Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Amita Kapoor, Sujit Pal
Book Image

Deep Learning with TensorFlow 2 and Keras - Second Edition

By: Antonio Gulli, Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
17
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18
Index

Using pretrained TPU models

Google offers a collection of models pretrained with TPUs available on GitHub TensorFlow/tpu repo (https://github.com/tensorflow/tpu). Models include image recognition, object detection, low-resource models, machine translation and language models, speech recognition, and image generation. Whenever it is possible, my suggestion is to start with a pretrained model [6], and then fine tune it or apply some form of transfer learning. As of September 2019, the following models are available:

Image Recognition, Segmentation, and more Machine Translation and Language Models Speech Recognition Image Generation

Image Recognition

  • AmoebaNet-D
  • ResNet-50/101/152/2000
  • Inception v2/v3/v4

Object Detection

  • RetinaNet
  • Mask R-CNN

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