## News Classification with Simple Neural Network

News Classification with Simple Neural Network is one of the application of Deep Learning. And here in this part of the blog, I am going to perform a Nepali News Classification. Before jumping into the main part, I would love to share some of my previous contents based upon which this blog has been written. […]

## Stock Price Prediction On Commercial Data Using RNN

A type of neural network called a recurrent neural network (RNN) uses the output from a previous step as input for the current step. Traditional neural networks have inputs and outputs that are independent of one another, but there is a need to remember the previous words in situations where it is necessary to anticipate […]

## Stock Price Prediction On Commercial Data Using GRU

GRU are a kind of Neural Networks which are designed to overcome some issues with RNN. There are two primary issues with recurrent neural networks Calculations of gradients either fail or explode. Gradient calculations are expensive Gradient clipping is a solution to the expanding gradient problem, and other topologies like the gated recurrent unit (GRU) […]

## Stock Price Prediction On Commercial Data Using LSTM

LSTMS are a variety of RNNs but lets start with RNNs. RNNs are unable to remember long-term dependencies in time series data because of the vanishing gradient issue. An RNN version called LSTM was created to deal with this problem. Similar to RNN, LSTM features a hidden state that functions as short-term memory. Additionally, it […]

## Multilayer Perceptron: Solving XOR Problem

Multilayer Perceptron: Solving XOR Problem from Scratch in Python In this blog we are going to explore how non-linear problem like XOR can be solved using multi layer perceptron. We have already tried how to apply multi layer perceptron on majority function please have a look here. We all are familiar with single layer perceptron […]

## Multilayer Perceptron Using Majority Function From Scratch

Multilayer Perceptron (MLP) We all know that single layer perceptron are commonly used to classify problems that are linearly separable. If we choose a single layer perceptron for a non-linearly separable problem, the results may not be successful. As a result, we must look for an alternative solution to a non-linear problem, and one such […]

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