The Vanishing Deep

Viel Spaß mit einem Buch in der Hand. Mach es mit dem Buch The Vanishing Deep doppelt so angenehm! Schließlich enttäuscht Astrid Scholte nie. Laden Sie das Online-Buch The Vanishing Deep herunter und lesen Sie es!
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BESCHREIBUNG

Astrid Scholte, author of Four Dead Queens, brings fans a thrilling new standalone YA where the dead can be revived...for a price. Tempest is a talented diver and with her older sister Elyssea, she has explored the ruins of many cities on the ocean floor, searching for anything of value to sell in the salt crusted markets on Zenith Reef. However, after Elyssea mysteriously drowns, Tempest is forced to dive and scavenge alone, barely scraping by. But when she finds a real living green plant at her dive site, an item so rare it is almost priceless, she finally has the money to have all her questions answered--by her dead sister. For the price of 3,000 notes, the research facility on the island of Palindromena will revive the departed for 24 hours before returning them to death. It is a time for the dead to make peace with the past and let the living move forward. Strong-willed Elyssea has other plans. Convincing Tempest to break her out of the facility, they embark on a dangerous adventure to find out the truth about their supposedly deceased parents, the secret behind the revival process, and the true price for restored life.
INFORMATION

VERÖFFENTLICHUNGSDATUM: 03.03.2020

AUTOR: Astrid Scholte

DATEIGRÖSSE: 3,91 MB

DATEINAME: The Vanishing Deep.pdf

HERUNTERLADEN ONLINE LESEN

The Vanishing Gradient Problem. The Problem, Its Causes, Its Significance, and Its Solutions . Chi-Feng Wang. Follow. Jan 8, 2019 · 3 min read. Title Image // Source. The problem: As more layers using certain activation functions are added to neural networks, the gradients of the loss function approaches zero, making the network hard to train. Why: Certain activation functions, like the ...

There are also ways to detect whether your deep network is suffering from the vanishing gradient problem. Photo by David Besh on Unsplash. The model will improve very slowly during the training phase and it is also possible that training stops very early, meaning that any further training does not improve the model. The weights closer to the output layer of the model would witness more of a ...

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