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This project involves the implementation of efficient and effective MLP (multi-layer perceptron) on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
Feature extraction of surface defect images based on Grey-Level Co-occurrence Matrix(GLCM) and classification using multi-layer perceptron and k-nearest neighbor classifier
In this project, I have created a neural network that classifies real world images digits. I have used MLP and CNN concepts in building, training, testing, validating and saving your Tensorflow classifier model.
A project that focuses on implementing a hybrid approach that modifies the identification of biomarker genes for better categorization of cancer. The methodology is a fusion of MRMR filter method for feature selection, steady state genetic algorithm and a MLP classifier.
A collection of fundamental Machine Learning Algorithms Implemented from scratch along-with their applications for various ML tasks like clustering, thresholding, data analysis, prediction, regression and image classification.
Spam SMS Detection model is a powerful solution built to identify and classify spam messages using the Naive Bayes algorithm. The accompanying Flask interface provides users with a seamless experience for submitting SMS entries, tracking usage, and receiving real-time classification results.