Category: Deep Learning

Neural networks

What are Neural Networks? | Strong and Jovial Plain Text

The human brain is an impressive feat of cognitive engineering, giving us the upper-hand when it comes to coming up with original ideas and concepts. We’ve even managed to create the wheel – something that not even our robot friends could do! This shows just how far we’ve come in terms of evolution – proving that humans are true masters of invention.

Top 5 Deep Learning Applications on Social Media For Businesses

Deep Learning – “It is undeniably mind-blowing” machine learning technique that teaches computers to do what comes naturally to humans: learn by example. It can […]

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Generative Adversarial Networks: The Art of Powerful AI Creativity

Generative Adversarial Networks (GANs) consist of two main components: a generator network and a discriminator network. The generator network generates synthetic data samples, while the discriminator network aims to distinguish between real and fake data. The two networks are trained simultaneously in an adversarial process, pushing each other to improve their performance. Here is a detailed explanation of the architecture and components of GANs.

2021 The Year of Transformers – Deep Learning

Transformers are a type of neural network architecture that has gained significant popularity due to their unwavering dedication to achieving optimal results in completing assigned tasks. Deep learning, which is widely recognized as a powerful tool, has significantly transformed the way we operate, proving to be both a lifesaver and a solution to disaster. Big players like OpenAI and DeepMind employ Transformers in their AlphaStar applications. …

Neural Networks vs Deep Learning

Deep Learning Vs Neural Networks – Demystifying the Differences

Deep learning, also called a subset of machine learning which is a specialist with an extremely complex skillset in order to achieve far better results from the same data set. It purely on the basis of NI (Natural Intelligence) mechanics of the biological neuron system. It has a complex skill set because of methods it uses for training i.e. learning in deep learning is based on “learning data representations” rather than “task-specific algorithms.”

Deep Learning – Driving the Innovation in NLP

Natural language processing, one of the most important technologies of today’s information age. It’s everywhere and used on almost at every instance in daily life like emails, machine translation, google search, virtual agents etc. In recent times deep learning has obtaining too much attraction and respect from the industry which helps nlp to avoid traditional, task-specific feature engineering. The performance across many different NLP tasks, using single end-to-end neural model has achieved significant improvement.

Deep Learning Algorithms — The Basic Guide

Deep learning leverages autonomous learning mechanisms that depend on simulated neural networks, commonly referred to as artificial neural networks (ANNs), to replicate the intricate cognitive operations of the brain implicated in information processing. During the process of training, algorithms endeavor to ascertain significant attributes, organize entities, and unveil consequential patterns within the data via the utilization of latent components in the input distribution.

Recursive Neural Network

Deep Learning – Introduction to Recursive Neural Networks

Recursive neural networks belong to the same family of models as deep neural networks, given that they can be seen as a modification of them. Usually, these results are produced by systematically applying a consistent set of weights to the arranged inputs in a repetitive manner. This occurrence happens consistently across all points due to identical causative elements. Recurrent Neural Networks (RNNS) are a group of structured designs that operate on directed acyclic graphs, tailored to handle organized inputs.

Artificial Neural Networks – Debunking The Myth

ANNs are an emerging discipline and they are the subject of research, study, and emulation for the information-processing capabilities of neurons of the human brain. Sadly many researchers are too quick and pivot the “Human Brain and ANNs” under one pin tip which is causing & creating huge confusion for newcomers. Their point in forms of discovery is entirely different though.

Deep Learning – Deep Convolutional Generative Adversarial Networks Basics

Generative Adversarial Networks are a class of algorithms used in the unsupervised learning environment. As the name suggests they are called Adversarial Networks because they are made up of two competing neural networks. Both networks compete with each other to achieve a zero-sum game..

Deep Learning – Backpropagation Algorithm Basics

Backpropagation Algorithm – An important mathematical tool for making better and high accuracy predictions in machine learning. This algorithm uses supervised learning methods for training Artificial Neural Networks. The whole idea of training multi-layer perceptrons is to compute the derivatives of the error function or gradient descent concerning weights using the backpropagation algorithm. This algorithm is actually based on the linear algebraic operation with a goal of optimising error function by harnessing its intelligence and provisioning updates.

Deep Learning – Introduction to Recurrent Neural Networks.

Deep Learning – Introduction to Recurrent Neural Networks

“Artificial neural networks (ANNs) are biologically inspired computing code with number of simple, highly interconnected processing elements for simulating human brain workings to process information model”.

Artificial Neural Networks - Everything You Need To Know

How Neural Network Algorithms Works: An Overview

Artificial neural networks are a type of computing model that takes inspiration from the structure and function of the neural networks found in the human brain. Nevertheless, machine learning has yet to attain authentic biological accuracy, given the present level of implementation and utilization. The process entails receiving multiple inputs and generating a single output.

Generative Adversarial Networks (GANs) - The Basics You Need To Know

Deep Learning – Introduction to Generative Adversarial Networks (GANs)

GANs consist of two neural networks i.e. Generator that generates a fake image of our currency note example and a disa criminator that classifies it into real or fake. The generator’s role is to map the input to the desired data space (image as in the example above). On the other hand second neural network models i.e. the discriminator classify the output with probability as real or fake compared with real datasets.

Deep Learning

Deep Learning (DL) – Introduction to Basics

AILabPage defines Deep learning is “Undeniably a mind-blowing synchronisation technique applied on data with computing power, skills and experience which practically has no limits“.