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In the bustling world of digital media, the term content is ubiquitous. While content creation is about generating material, context creation involves crafting the setting and conditions that allow the content to be meaningful and impactful.

In 2012, the field of artificial intelligence witnessed a seismic shift. The catalyst for this transformation was a deep learning model known as AlexNet.

A neural network is like a highly sophisticated, multi-layered calculator that learns from data. It consists of numerous “neurons” (tiny calculators) connected in layers, with each layer performing a unique function to help the network make predictions or decisions.

The beauty of VAEs lies in their ability to generate new samples by randomly sampling vectors from this known region and then passing them through the generator part of our model.

An autoencoder begins its journey by compressing input data into a lower dimension. It then endeavors to reconstruct the original input from this compressed representation.

Delaunay triangulation is a process that takes a set of points in n-dimensional space as input and returns a network of triangles connecting these points. The resulting structure is called a triangulation or mesh.

Paltering reminds us that even truthful statements can be used to mislead. Understanding the context and limitations of facts, and the role of scientific method in informing but not dictating actions, is essential.

In today’s fast-paced world, getting things done is crucial. The Done manifesto is all about embracing this mindset and applying it to your work.

Stippling, a timeless artistic technique, traces its origins back through the annals of art history, where it emerged as a method of creating texture, depth, and form through the precise placement of dots.

Hidden Markov Models (HMMs) are statistical models used for sequential data analysis, where underlying states are inferred from observed data. Employed in speech recognition, bioinformatics, and more.