Convolutional Neural Networks (CNNs) are a type of deep learning model designed to process grid-like data, such as image

How efficiently can we mimic biological spiking process of neurons and synapses, and is CMOS a good choice for neural networks

Graphs are a powerful tool to represent data, but machines often find them difficult to analyze. Explore graph neural networks, a deep-learning method designed to address this problem, and learn about the impact this methodology has across

Dropout layers have been the go-to method to reduce the overfitting of neural networks. It is the underworld king of regularisation in the

Artificial neural networks and Markov ProcessesI read a little about ANN and Markov process. Can someone please help me in

Telesens convolutional neural networks Initializing Weights for the Convolutional and Fully Connected Layers April 9, 2018 ankur6ue 0 You may have noticed that weights for convolutional and fully connected layers in a deep neural network (DNN) are initialized in a specific way. For […] Search Search for: About This Site Welcome to my blog! My name is Ankur and I love to write about ML/AI algorithms and Cloud Computing technologies, as well as travel stories (see “Stories” section). I hope you find my

Graph Neural Networks Revolutionize Product Recommendations with Dual Embeddings. Dual embeddings in graph neural networks are revolutionizing product r

The most intuitive explanation of the mathematical prowess that are neural networks

Explanation of Convolutional Neural Networks in the field of image processing, including an example calculation of the convolution layer

You might remember that convolutional neural networks are more successful than conventional ones. Can I adapt convolutional neural networks to unlabeled images for clustering? Absolutely yes! these customized form of CNN are convolutional autoencoder