A guide to some of the most important concepts to demystify the technology that has everyone talking

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Artificial neural networks (ANNs) are the cornerstone of Deep Learning algorithms. The name and the architecture are adopted from the human brain's neural network. ANNs are designed to simulate human reasoning based on how neurons communicate. ANNs contain a set of artificial neurons connected.

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The magic of artificial intelligence brings us better Netflix recommendations, better cancer diagnoses, and the ability to becomes gods by creating our own AI-powered creatures and ...

Artificial Neural Networks mimic the human brain, processing data through interconnected neurons for adaptive learning in machine learning.

They succeeded in reducing the delay between a change in brain activity and the corresponding feedback signal by a factor of 50

An introduction to neural networks. Understand the math behind convolutional neural networks with forward and backward propagation & Build a CNN using NumPy.

According to a study published in Radiology, researchers have succeeded in combining imaging information with clinical patient data to improve diagnostic performance on chest X-Rays.

Digital technologies such as Deep Learning and Machine Learning have taken the world by storm. Although these technologies are in their nascent stage,

Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances…

Introduction to tensorflow and Implementing deep learning using tensorflow. Learn how to implement neural networks using TensorFlow in python.

Neural Networks from Scratch with Python for Beginners Download free Notes. With the Help of You will Learn about Complete Neural Network.

Today, we will discuss What is Neural Network? in detail, as understanding neural network is necessary for deep learning understanding.

Curating the neural-architecture search space and taking advantage of human intuition reduces latency on real-world applications by up to 55%.

ChainGPT is an AI tool that is revolutionizing the way we work. It is a powerful language model that can generate human-like text with a high degree of accuracy. It uses an advanced neural network to produce text that is virtually indistinguishable from that written by a real person. With ChainGPT, you can create content that is engaging, informative, and tailored to your specific needs. The tool is incredibly versatile and can be used for a variety of tasks, such as generating blog posts, articles, emails, and more. It can even be used to create custom chatbots for customer service. ChainGPT is an invaluable tool for businesses and organizations looking to create content that is both engaging and effective. It is easy to use and can be used to quickly and efficiently create content that is sure to engage your audience.

Transformer-based neural networks have received much attention lately because they function well. Machine translation, text creation, and question answering are just a few natural language processing activities for which Transformer architecture (see figure 1) has emerged as the industry standard. The effectiveness of transformer-based models is not restricted to NLP; they have also been used successfully in several other fields, such as speech recognition, computer vision, and recommendation systems. Large language, vision, and multimodal foundation models are the most complex and effective of these models, with billions to trillions of parameters. Each new model, however, is typically taught from the

Artificial intelligence (AI) has taken on an increasingly central role in chemistry research and industrial development in the last few decades. In particular, analytical chemistry and biochemistry for life sciences have integrated tools like machine learning algorithms and artificial neural network

In recent decades, machine learning and deep learning algorithms have become increasingly advanced, so much so that they are now being introduced in a variety of real-world settings. In recent years, some computer scientists and electronics engineers have been exploring the development of an alternative type of artificial intelligence (AI) tools, known as diffractive optical neural networks.

Dive into this article for a comprehensive exploration of neural networks and the process of building and training these powerful computational models.

So my last article was a very basic description of the MLP. In this article, I’ll be dealing with all the mathematics involved in the MLP. For those who haven’t read the previous article, you can…

Artificial Intelligence has been witnessing a monumental growth in bridging the gap between the capabilities of humans and machines…

Here, the authors demonstrate a low-power neuromorphic visual architecture based on a plasmon-enhanced 2D semiconductor phototransistor array, showing high-speed sensing, preprocessing and image recognition functionalities.

We’ve been wasting our processing power to train neural networks that are ten times too big.

The zoo of neural network types grows exponentially. One needs a map to navigate between many emerging architectures and approaches. Fortunately, Fjodor van Veen from Asimov institute compiled a…

This post is the outcome of my studies in Neural Networks and a sketch for application of the Backpropagation algorithm. It’s a binary classification task with N = 4 cases in a Neural Network with a single hidden layer. After the hidden layer and the output layer there are sigmoid activation functions. Different colors were… Read More »Matrix Multiplication in Neural Networks

How neural networks recognize a dog in a photo

Dive into this article for a comprehensive exploration of neural networks and the process of building and training these powerful computational models.

Deep Learning is amazing. But why is Deep Learning so successful? Is Deep Learning just old-school Neural Networks on modern hardware? Is it just that we have so much data now the methods work b…

With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely […]

Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and…

Broken down in terms every non-technical person can understand. “A Beginners Guide to Neural Networks” is published by Thomas Davis in Towards Data Science.