Tag: Gradient

Free Download Gradient Smoothing Meethods with Programming : Applications to Fluids and Landslides (287 Pages)
by G R Liu & Zirui Mao
English | 2024 | ISBN: 9811280002 | 288 pages | True PDF | 162.45 MB

Free Download Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks by Keith L. Downing
English | July 18, 2023 | ISBN: 0262545616 | 224 pages | MOBI | 13 Mb
An insightful investigation into the mechanisms underlying the predictive functions of neural networks-and their ability to chart a new path for AI.

Free Download Blend Without Blending: Fun Gradient Techniques for Flat Illustration.
File Name:Blend Without Blending: Fun Gradient Techniques for Flat Illustration —>Home Page :https://www.skillshare.com/en/classes/Blend-Without-Blending-Fun-Gradient-Techniques-for-Flat-Illustration/1434440727 —>Genre / Category: Drawing & Painting , Art —>File Size :471 MB–>Publisher:skillshare| Updated and Published:December 30, 2023 –>Product Details
In this unique and innovative class, we will explore techniques that allow you to blend two blocks of color without physically mixing the colors together. These techniques are particularly useful for illustration mediums that are challenging to blend, such as acrylic paint markers (for example, POSCA markers). However, they can be applied to any opaque medium including acrylic paints, acrylic gouache, pen and ink, paper cutting or even digital drawing tools like Procreate or Adobe Fresco.

Free Download Gradient Expectations: Structure, Origins, and Synthesis of Predictive Neural Networks
English | 2023 | ISBN: 0262545616 | 225 Pages | PDF (True) | 6.4 MB
Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend this capability more explicitly into synthetic neural networks to improve the function of AI and enhance its place in our world?Gradient Expectations is a bold effort by Keith L. Downing to map the origins and anatomy of natural and artificial neural networks to explore how, when designed as predictive modules, their components might serve as the basis for the simulated evolution of advanced neural network systems.