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Tag Archives: Neural Network

The ANN(Artificial Neural Network) is based on BNN(Biological Neural Network) as its primary goal is to fully imitate the Human Brain and its functions. Similar… Read More
Neural architecture and search methods The effort of automatically selecting one or more designs for a neural network that would generate models with good outcomes… Read More
As we know while building a neural network we are doing convolution to extract features with the help of kernels with respect to the current… Read More
Since their inception in the late 1950s, Artificial Intelligence and Machine Learning have come a long way. These technologies have gotten quite complex and advanced… Read More
Deep learning has been on the rise in this decade and its applications are so wide-ranging and amazing that it’s almost hard to believe that… Read More
Recurrent neural network (RNN) is more like Artificial Neural Networks (ANN) that are mostly employed in speech recognition and natural language processing (NLP). Deep learning… Read More
In this article, we are going to implement and train a convolutional neural network CNN using TensorFlow a massive machine learning library. Now in this… Read More
Keras is an open-source API used for solving a variety of modern machine learning and deep learning problems. It enables the user to focus more… Read More
Beta Variational Autoencoders was proposed by researchers at Deepmind in 2017. It was accepted in the International Conference on Learning Representations (ICLR) 2017. Before learning… Read More
Variational Autoencoders Variational autoencoder was proposed in 2013 by Knigma and Welling at Google and Qualcomm. A variational autoencoder (VAE) provides a probabilistic manner for… Read More
The basic neural network design i.e. an input layer, few dense layers, and an output layer does not work well for the image recognition system… Read More
Adline stands for adaptive linear neuron. It makes use of linear activation function, and it uses the delta rule for training to minimize the mean… Read More
Deep Parametric Continuous Kernel convolution was proposed by researchers at Uber Advanced Technologies Group. The motivation behind this paper is that the simple CNN architecture… Read More
Prerequisites: RNN The Hopfield Neural Networks, invented by Dr John J. Hopfield consists of one layer of ‘n’ fully connected recurrent neurons. It is generally… Read More
Contractive Autoencoder was proposed by the researchers at the University of Toronto in 2011 in the paper Contractive auto-encoders: Explicit invariance during feature extraction. The… Read More

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