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Python implementation for hopfield neural network

This is the second stage that dictates the selection of relevant feature vectors to train the predictive classifier. The relevant features are obtained from stock data and sentimental data. The objective of this stage is to find out the frequent trend patterns based on close price of a day with increased recall capacity, which is helpful for the time-series data. Here, a novel Hopfield Neural Networks using technical indicator, Exponentially Weighted Moving Average (EWMA) is proposed.

Skills: Python, Machine Learning (ML), Algorithm, Software Architecture, Neural Networks

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eecs93

My preferred method of freelancing is an interactive approach to project solving. I have an MSEE specializing in Digital Signal/Image Processing. I do my work in MATLAB (expert). I also do Python programming. I loo More

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datacode0023

Hi, I have +5 years of experience dealing with machine learning algorithms and worked on multiple projects in this field, I absolutely can do your project as you like. Please contact me to discuss more. Have a nice day

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pedrovalecci

I can build this wizard all in python with all the desired functions. we can add a feature that optimizes the schedule

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