neural networks

A Deep Dive into the most popular architectures driving data science advancements In the ever-evolving field of data science, neural networks stand as the backbone of many advanced machine learning applications. These intricate structures, inspired by the human brain, have led to groundbreaking achievements in various domains. Convolutional Neural Networks (CNNs): Revolutionizing image and video analysis, Convolutional Neural Networks (CNNs) have become a cornerstone in computer vision. With their ability…

Trained neural networks arrive at solutions that achieve superhuman performance on an increasing number of tasks. It would be at least interesting and probably important to understand these solutions.  Interesting, in the spirit of curiosity and getting answers to questions like, “Are there human-understandable algorithms that capture how object-detection nets work?” This would add a new modality of use to our relationship with neural nets from just querying for answers (Oracle Models) or sending on tasks (Agent Models)…

Neuromorphic computing is an intriguing and fast-expanding area that creates brain-like computer systems by drawing inspiration from the human brain. In this essay, we will look at the basics of neuromorphic computing, its components, and its applications in artificial intelligence and computing. Neuromorphic Computing Basics Terminology Before we delve into the structure of neuromorphic computing, let’s familiarize ourselves with some key terminology: Neuromorphic Hardware: Specialized hardware designed to mimic the…

Elevate your Python AI skills: A complete guide to neural network development and prediction Neural networks are a type of artificial intelligence (AI) that can learn from data and perform complex tasks, such as image recognition, natural language processing, and speech synthesis. In this article, we will explore on  Python AI for neural networks, a popular and versatile programming language, to create and train neural networks, and use them for prediction and inference. What is a Neural Network? A neural…

Welcome back to this series where we are learning how to integrate AI tooling into web applications. In the previous posts, we got our project set up and did some basic integration. Intro & Setup Your First AI Prompt Streaming Responses How Does AI Work Prompt Engineering AI-Generated Images Security & Reliability Deploying So far we’ve built a very basic UI with a text area that takes whatever the user writes and sends it over HTTP to OpenAI’s API. When the streaming response returns, it…

AI holds significant promise for the IoT, but running these models on IoT semiconductors is challenging. These devices’ limited hardware makes running intelligent software locally difficult. Recent breakthroughs in neuromorphic computing (NC) could change that. Even outside the IoT, AI faces a scalability problem. Running larger, more complex algorithms with conventional computing consumes a lot of energy. The strain on power management semiconductors aside, this energy usage leads to sustainability and cost…

Imagine spending a weekend afternoon with friends at an art museum: nodding with crossed arms, desperately searching for something insightful to say. The vast majority of paintings you stroll past are immediately forgotten, but some stick in your mind. As it turns out, the paintings you remember are likely the same ones everyone else does.There’s a scientific term for that: image memorability. “It’s the idea that, essentially, there are some intrinsic patterns that make some content more memorable than others,” says…

Neural networks and deep learning have revolutionized the field of artificial intelligence and machine learning by enabling remarkable advancements in various domains.  This research article aims to comprehensively introduce the fundamentals of neural networks and deep learning.  We start with the basic building blocks of neural networks and delve into the concepts of neurons, activation functions, and layers.  Subsequently, we explore the deep learning models' architecture and working principles, emphasizing their…

Graham tried tweaking the CNN approach so that the kernel would only be placed on 3-by-3 sections of the image that contain at least one pixel that has nonzero value (and is not just blank). In this way, he succeeded in producing a system that could efficiently identify handwritten Chinese. It won a 2013 competition by identifying individual characters with an error rate of only 2.61 percent. (Humans scored 4.81 percent on average.) He next turned his attention to an even bigger problem: three-dimensional-object…

In 2014, DeepMind was acquired by Google after demonstrating striking results from software that used reinforcement learning to master simple video games. Over the next several years, DeepMind showed how the technique does things that once seemed uniquely human—often with superhuman skill. When AlphaGo beat Go champion Lee Sedol in 2016, many AI experts were stunned, because they had believed it would be decades before machines would become proficient at a game of such complexity.New ThinkingTraining a large language…