Tag: Neural

A Step-by-Step Guide Deep learning has revolutionized the field of artificial intelligence and has found applications in various domains such as image recognition, natural language processing, and recommendation systems. JavaScript, a versatile and widely-used programming language, is no exc...

Neural Nets sound sexy and interesting. But what are they exactly? How do they achieve their magic and most importantly, can you build one without being an expert data scientist? In this article I’m going to show you the basics of neural networks and how you can implement one yourself using...

Before we talk about the cutting edge technology that is responsible for the AI craze, let’s first take a step back and explore the foundational concepts that paved the way for its emergence. At the core of every electronic device is a Digital Circuit. Digital circuits are the cornerstones for...

Data scientists use neural networks as a tool to analyze photos and images. A neural network is a machine learning tool used to analyze images. It tries to focus on essential features of an image and determine a value or class to it. In this case, we will go through an example of classifying clothes...

A fundamental issue with LegalTech is that words — the basic currency of all legal documentation — are a form of unstructured data that cannot be intuitively understood by machines. Therefore, in order to process textual documents, words have to be represented by vectors of real numbers....

As self-driving is going to be part of everyday life, safety has to be guaranteed in every possible driving condition. One key component of self-driving is the perception of the environment, as planning is based on the perception and acting is based on planning. For sensing, the collection of 3-dime...

This article covers Hopfield Networks — recurrent neural networks capable of storing and retrieving multiple memories. We’ll begin with an in-depth conceptual overview, then move to an implementation of Hopfield Networks from scratch in python; here we’ll construct, train, animate,...

Although artificial neurons and perceptrons were inspired by the biological processes scientists were able to observe in the brain back in the 50s, they do differ from their biological counterparts in several ways. Birds have inspired flight and horses have inspired locomotives and cars, y...

The world of artificial intelligence is rapidly changing, especially when with it comes to another branch of neural network that is beginning to gain attention: spiking neural networks (SNNs). In this comprehensive guide, we’ll explore what SNNs are, their neuroscience basis, modeling techniqu...

In the realm of user experience (UX) design, the integration of neuroscience, particularly the principle of neural entrainment and its role in multisensory integration, presents a groundbreaking opportunity to redefine digital interactions. This concept, though not widely recognized in mainstream UX...

In this article, I will walk through the development of an artificial neural network from scratch using NumPy. The architecture of this model is the most basic of all ANNs — a simple feed-forward network. I will also show the Keras equivalent of this model, as I tried to make my impl...

This post is based on recent works with Cristian Bodnar, Xiaowen Dong, Ben Chamberlain, Davide Eynard, Fabrizio Frasca, Francesco Di Giovanni, Maria Gorinova, Pietro Liò, Giulia Luise, Sid Mishra, Guido Montúfar, Emanuele Rossi, Konstantin Rusch, Nina Otter, James Rowbottom, Jake Toppi...

Additive manufacturing is a promising method for developing metamaterials: while the material (resin, polymer etc.) printed by the machine is typically of one type (with a predetermined stiffness etc.), we can achieve varying properties and compressive behaviours by changing the geometry of the prin...

Neural networks have become ubiquitous in our lives, but their inner workings are still baffling even to many practitioners. In this post, I’ll explain how Feedforward Neural Networks conceptually work using just basic linear algebra and geometry. At their core, these networks lea...

I have a master's degree in physics and work as an aerospace engineering researcher. Physics and engineering are two distinct sciences that share a desire to understand nature and the ability to model it. The approach of a physicist is more theoretical. The physici...

There are many ways in which an artificial neural network (ANN) can break down and not perform well. In this blog, we go through some of the essential requirements for getting a feed-forward ANN to work properly for a typical classification problem. The dataset chosen was the “Large Soybean Da...

Liquid neural nets (LNNs) are an exciting, relatively new direction in AI/ML research that promises more compact and dynamic neural nets for time series prediction. LNNs offer a new approach to tasks like weather prediction, speech recognition, and autonomous driving. The primary benefit LNNs o...

Unveiling the Brain’s Secrets and the Path to Next-Generation AI In the quest to unravel the complexities of the human brain, a tool older and more sophisticated than any technology at our disposal, Cristina Savin’s Artificial & Biological Computation lab at NYU stands a...