Gana016
@ganapathi12Computer Science Engineering Student 👨🎓, Machine learning Practitioner 👨🏫 , Full Stack Dev ⚛️(React and Django).
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codeimmortal
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R Sree Ranjani
@ranjaninambiar
Shanmuka Abhinay Potti
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Jagadeeshram D
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Harish K
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Software Engineering project for helping faculty to manage courses.
The standard example for machine learning these days is the MNIST data set, a collection of 70,000 handwriting samples of the numbers 0-9. now we predict which number each handwritten image represents.Each image is 28x28 grayscale pixels, so we treat each image as just a 1D array, or tensor, of 784 numbers.MNIST provides 60,000 samples in a training data set, 10,000 samples in a test data set, and 5,000 samples in a "validation" data set. We haven't talked about validation sets before, but their intent is to be used for model selection. So you'd use validation data to select your model, train the model with the training set, and then evaluate the model using the test data set.The training data, after we "flatten" it to one dimension using the reshape function, is therefore a tensor of shape [60,000, 784] - 60,000 instances of 784 numbers that represent each image. we define our architecture by 1 hidden layer and we use relu for activating nodes and we use 20 epochs and keep batch size of 100.
Students view for Digital-course-file-system
This is project on mask detection, done by me and my teamate Balaji Dass for a mlh hackathon,Successfully trained yolo model with custom datasets gathred from google and kaggle.
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this dataset is downloaded from kaggle and in this dataset containes all the customer id's and the reviews given, and whether the review is positive, negative or neutral and we need to predict which part of the sentence is reponsible for the review being positive, negative or neutral and this can be simply done by using sentiment analyzer in sklearn library but the accuracies are too low so alternatively this can be done by using lstm
this is a python code which uses haarcascade files for eye,face and smile detection with the help of opencv we are utilizing these haarcascade files for some face recognization and this calcluations are done on cpu and the input is obtained from the webcam the input video is fragmented into frames and these frames are converted into gray images and calculations are done on these grey images and the result is projected back into video on window with the help of opencv
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