Elastic’s Post

📝New blog post: https://lnkd.in/giKr4ajm From personalized learning paths to consistent, scalable solutions, AI is revolutionizing how teams learn and grow.

🏢 High-density colocation provides several crucial advantages for organizations that rely on AI and ML workloads. These benefits directly address the demanding computational requirements of advanced applications. 👉 Read more: https://buff.ly/4hg1Us1 #CoveredWithCanopy #Colocation #HighDensity #Educational #AI #MachineLearning #DataCanopy

Ever wonder how to make your AI smarter? Discover a simple yet powerful phrase that can significantly enhance your AI's reasoning abilities, especially when dealing with complex problems. Learn how to transform vague answers into clear, logical steps, ensuring more accurate and reliable outcomes. #openai #llms #generativeai

Ever wonder how to make your AI smarter? Discover a simple yet powerful phrase that can significantly enhance your AI's reasoning abilities, especially when dealing with complex problems. Learn how to transform vague answers into clear, logical steps, ensuring more accurate and reliable outcomes. #openai #llms #generativeai

Need help with understanding the structure and working of ANNs, here is the key!!!

Highly simplified and useful visualization of how neural networks do their thing. Note that this only shows what happens at “runtime.” Does not get into how neural nets are trained.

In the 80s, I worked for a corporation specializing in OCR (Optical Character Recognition) called Recognition Equipment Incorporated. One of the devices we had, the S9100, used a photodiode to detect the black and white patterns of characters. It also included a resistance matrix—a hardware-based pattern-matching mechanism—and firmware with all the character patterns encoded. By matching patterns, the system identified characters. This concept of pattern matching has since transitioned from hardware to software, forming the foundation of modern OCR technology

Neural Network - How it works - Visual of it

Simple visualization of how various neurons activate in a neural network to classify the digits

What is an Artificial Neural Network (ANN)? An Artificial Neural Network (ANN) is a computational model inspired by the structure and function of the human brain. It consists of interconnected nodes or "neurons" that process and transmit information. Components of an ANN 1. *Artificial Neurons (Nodes)*: These are the basic computing units of the network. Each node receives one or more inputs, performs a computation, and produces an output. 2. *Connections (Synapses)*: These are the links between nodes. Each connection has a weight associated with it, which determines the strength of the signal transmitted between nodes. 3. *Activation Functions*: These are mathematical functions that determine the output of each node based on the inputs it receives. By increasing the number of neurons in a network we have so much of positive effects like: 1. *Improved accuracy*: More neurons can learn more complex patterns in the data, leading to improved accuracy. 2. *Increased capacity*: More neurons can store more information, allowing the network to learn and remember more complex relationships.