Neural Networks and Deep Learning (Springer)
Monday, 19 November 2018

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. Author Charu C. Aggarwal looks at why neural networks work, and when they work better than off-the-shelf machine-learning models. He also considers when depth is useful, why training neural networks is so hard, and what the pitfalls are. The book also looks at different applications to give a flavor of how neural architectures are designed for different types of problems such as recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics.

<ASIN:3319944622>

 

Author: Charu C. Aggarwal
Publisher: Springer
Date: August 2018
Pages: 497
ISBN: 978-3319944623
Print: 3319944622
Kindle:  B07FKF5HY7
Audience: graduate students, researchers, and neural network practitioners
Level: Advanced
Category:Artificial Intelligence 

 

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Web Scraping with Python (2e)

Author: Ryan Mitchell 
Publisher: O'Reilly
Pages: 308
ISBN: 978-491985571
Print: 1491985577
Kindle: B07BMGBYSK
Audience: Budding web scrapers
Rating: 4
Reviewer: Alex Armstrong

Web scraping is a strange activity, but Python is a good choice of a language for it.



Improving Agile Retrospectives

Author: Marc Loeffler
Publisher: Addison-Wesley
Pages: 270
ISBN: 978-0134678344
Print: 0134678346
Kindle: B0785W7PM6
Audience: Developers using Agile
Rating: 4
Reviewer: Kay Ewbank

 

The use of retrospectives in agile development is one of the key ideas behind the methodology, and this book aims [ ... ]


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