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Showing posts from March, 2021

CIFAR10 Image Classifier using PyTorch

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     This is a blog post on the work that I have done in programming an image classifier for the CIFAR10 dataset.      I had followed many notebooks, tutorials, and guidance from my Teaching Assistant Mondol, Md Ashaduzzaman Rubel, and my fellow classmate Subbiah Sharavanan, Abishek Pichaipillai for creating  this notebook . The Work     First I had to work on a base tutorial code available on the PyTorch website here . It is a beginner tutorial for classifying images into their respective labels which are present in the CIFAR10 dataset. Also need to try different optimizers provided by PyTorch and document the results and infer some conclusions.      CIFAR10 dataset:      The CIFAR10 dataset consists of 6000 images with which there are 1000 tests and 5000 training images with labels.     It has images from 10 classes namely  plane, car, bird, cat, deer, dog, frog, horse, ship, and truck. The ...

Training a Machine

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Introduction A day ago I learned how to train a machine to close on a prediction. I thought it was easy as I just coded the math to narrow down on a prediction from the loss or the inaccuracy. The best thing was that I thought it would be very complicated. But I had understood it I did that tedious math by hand and coded it myself. The steps were as follows Analyze and predict the function. Assume certain values for the bias, constants, and step. Plugin the input to the prediction. Calculate the loss. Calculate the gradients for each of the constants and bias. Update the constants and the Bias by subtracting the respective gradients. Now repeat from step 3 till you get 0 loss. From the above steps, there are critical things that we need to note To analyze and predict the function of the data was done for me. The day was also a linear result so the function resulting was linear and predictable with the known function y = wx + b where w is the weight constant and b is the bias constant n...