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Tag Archives: Neural Network

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
Emotion Detection is one of the hottest topics in research nowadays. Emotion sensing technology can facilitate communication between machines and humans. It will also help… Read More
Neural Networks are a biologically-inspired programming paradigm that deep learning is built around. Python provides various libraries using which you can create and train neural… Read More
Hebbian Learning Rule, also known as Hebb Learning Rule, was proposed by Donald O Hebb. It is one of the first and also easiest learning… Read More
Prerequisite: ANN | Bidirectional Associative Memory (BAM) Learning AlgorithmTo implement BAM model, here are some essential consideration and approach-   Consider the value of M, as… Read More
DNN(Deep neural network) in a machine learning algorithm that is inspired by the way the human brain works. DNN is mainly used as a classification… Read More
GANs is an approach for generative modeling using deep learning methods such as CNN (Convolutional Neural Network). Generative modeling is an unsupervised learning approach that… Read More

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