Liu / Mehta Hands-On Deep Learning Architectures with Python

Create deep neural networks to solve computational problems using TensorFlow and Keras
1. Auflage 2019
ISBN: 978-1-78899-050-9
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 316 Seiten

No detailed description available for "Hands-On Deep Learning Architectures with Python".

Autoren/Hrsg.

Liu, Yuxi (Hayden)

Mehta, Saransh

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Table of Contents - Getting Started with Deep Learning
- Deep Feedforward Networks
- Restricted Boltzmann Machines and Autoencoders
- CNN Architecture
- Mobile Neural Networks and CNNs
- Recurrent Neural Networks
- Generative Adversarial Networks
- New Trends of Deep Learning

Über Autor(innen)

Liu Yuxi (Hayden):

Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.Mehta Saransh:

Saransh Mehta has cross-domain experience of working with texts, images, and audio using deep learning. He has been building artificial, intelligence-based solutions, including a generative chatbot, an attendee-matching recommendation system, and audio keyword recognition systems for multiple start-ups. He is very familiar with the Python language, and has extensive knowledge of deep learning libraries such as TensorFlow and Keras. He has been in the top 10% of entrants to deep learning challenges hosted by Microsoft and Kaggle.