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Deep Learning with TensorFlow 2 and Keras - Second Edition

You're reading from  Deep Learning with TensorFlow 2 and Keras - Second Edition

Product type Book
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Pages 646 pages
Edition 2nd Edition
Languages
Authors (3):
Antonio Gulli Antonio Gulli
Profile icon Antonio Gulli
Amita Kapoor Amita Kapoor
Profile icon Amita Kapoor
Sujit Pal Sujit Pal
Profile icon Sujit Pal
View More author details

Table of Contents (19) Chapters

Preface 1. Neural Network Foundations with TensorFlow 2.0 2. TensorFlow 1.x and 2.x 3. Regression 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Generative Adversarial Networks 7. Word Embeddings 8. Recurrent Neural Networks 9. Autoencoders 10. Unsupervised Learning 11. Reinforcement Learning 12. TensorFlow and Cloud 13. TensorFlow for Mobile and IoT and TensorFlow.js 14. An introduction to AutoML 15. The Math Behind Deep Learning 16. Tensor Processing Unit 17. Other Books You May Enjoy
18. Index

What is AutoML?

During the previous chapters we have introduced several models used in modern machine learning and deep learning. For instance, we have seen architectures such as Dense networks, CNNs, RNNs, Autoencoders, and GANs.

Two observations are in order. First, these architectures are manually designed by deep learning experts, and are not necessarily easy to explain to non-experts. Second, the composition of these architectures themselves was a manual process, which involved a lot of human intuition and trial and error.

Today, one primary goal of artificial intelligence research is to achieve Artificial General Intelligence (AGI) – the intelligence of a machine that can understand and automatically learn any type of work or activity that a human being can do. However, the reality was very different before AutoML research and industrial applications started. Indeed, before AutoML, designing deep learning architectures was very similar to crafting – the activity...

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