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Preface
Figure 5: Adoption of deep learning frameworks
Second, the increasing possibility of using managed services in the cloud (see
Chapter 12, TensorFlow and Cloud) with accelerators (Chapter 16, Tensor Processing
Unit). This allows data scientists to focus on ML problems with no need to manage
the infrastructural overhead.
Third, the increasing capacity of deploying models in more heterogeneous systems:
mobile devices, Internet of Things (IoT) devices, and even the browsers normally
used in your desktop and laptop (see Chapter 13, TensorFlow for Mobile and IoT and
TensorFlow.js).
Fourth, the increased understanding of how to use more and more sophisticated
DL architectures such as Dense Networks (Chapter 1, Neural Network Foundations
with TensorFlow 2.0), Convolutional Network (Chapter 4, Convolutional Neural
Networks, and Chapter 5, Advanced Convolutional Neural Networks), Generative
Adversarial Networks (Chapter 6, Generative Adversarial Networks), Word
Embeddings (Chapter 7, Word Embeddings), Recurrent Network (Chapter 8,
Recurrent Neural Networks), Autoencoders (Chapter 9, Autoencoders), and advanced
techniques such as Reinforcement Learning (Chapter 11, Reinforcement Learning).
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