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. …
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 with respect to weights using the backpropagation algorithm. This algorithm … Continue reading Deep Learning – Backpropagation Algorithm Basics
The year 2020 will be remembered as a significant period in human history due to its remarkable strides in progressive innovation, more commonly referred to as “Emerging Technologies.” In this modern era, industrial and business practitioners are expected to establish robust foundational structures for the integration of SMAC (Social, Mobile, Analytics, and Cloud) architecture. It is projected that the prevalence of selection will attain its apex during the decade spanning from 2010 to 2020. The implementation of computerized transformation is ...
Generative Adversarial Networks (GANs) – A very young family member of Deep Neural Networks Architecture. Introduce by Ian Goodfellow and his team at the University of Montreal in 2014. GANs are class of unsupervised machine learning algorithm. Adversarial training “The most interesting idea in the last 10 years in the field of Machine Learning.” … Continue reading Deep Learning – Introduction to Generative Adversarial Neural Networks (GANs)
Recurrent Neural Networks – Main use of RNNs are when using google or facebook these interfaces are able to predict next word what you are about to type. RNNs have loops to allow information to persist. RNN’s are considered to be fairly good for modeling sequence data. Recurrent neural networks are linear architectural variant of recursive networks. … Continue reading Deep Learning – Introduction to Recurrent Neural Networks
Convolutional Neural Network – In this article, we will explore our intuitive explanation of convolutional neural networks (CNN’s) on high level. CNN’s are inspired by the structure of the brain but our focus will not be on neural science in here as we do not specialise in any biological aspect. We are going artificial in this … Continue reading Deep Learning – Introduction to Convolutional Neural Networks
#AILabPage defines - “AI is kind of intelligence demonstrated by machines to do the same task done by any human using natural intelligence”. In other words same task performed by Human with Natural Intelligence and Machine with Artificial Intelligence
Without the right Information-Security and encryption solution in place; big data is a very big problem as well. What AILabPage propose and advocate for is DataIntelligence with DataSecurity as prime goals for any data factory.
Neural network based on radial basis function with can be used for strategic reasons. There are several other models of neural network including what we have mentioned above. For an introduction to neural network and their working model
Deep Learning (DL) – A very young and limitless field. Its a class of machine learning where theories of the subject aren’t strongly established and views quickly changes almost on daily basis. “I think people needs to understand that deep learning is making a lot of things, behind the scenes, much-better” – Sir Geoffrey Hinton What is Deep … Continue reading Deep Learning – Introduction to Basics
AILabPage 