pdfcoffee

Preface
An improvement of 10x per pod (see Figure 4) was achieved in one year only, which
allows faster training:
Figure 4: TPU accelerators performance in petaflops
However, DL's growth is not only in terms of better accuracy, more research papers,
larger models, and faster accelerators. There are additional trends that have been
observed over the last four years.
First, the availability of flexible programming frameworks such as Keras [1],
TensorFlow [2], PyTorch[8], and fast.ai; these frameworks have proliferated within
the ML and DL community and have provided some very impressive results, as
we'll see throughout this book. According to the Kaggle State of the Machine Learning
and Data Science Survey 2019, based on responses from 19,717 Kaggle (https://www.
kaggle.com/) members, Keras and TensorFlow are clearly the most popular choices
(see Figure 5). TensorFlow 2.0 is the framework covered in this book. This framework
aims to take the best of both worlds from the great features found in Keras and
TensorFlow 1.x:
[ xvi ]