MNIST Handwritten Digits Classification From Scratch using Python Numpy

MNIST Handwritten Digits Classification From Scratch using Python Numpy. SoI recently made a classifier for the MNIST handwritten digits dataset using PyTorch. Can I recreate the same model in vanilla Python? Of course, I was going to use NumPy for this.

So I recently made a classifier for the MNIST handwritten digits dataset using PyTorch and later, after celebrating for a while, I thought to myself, “Can I recreate the same model in vanilla python?” Of course, I was going to use NumPy for this. Instead of trying to replicate NumPy’s beautiful matrix multiplication, my purpose here was to gain a better understanding of the model by reinventing the wheel.

I challenged myself to make a similar classifier in numpy and learn some of the core concepts of Deep Learning along the way. You can find the code in my GitHub repository.

deep-learning python numpy backpropagation mnist-dataset

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benchmark visual recognition datasets for deep learning Caltech101, Caltech256, CaltechBirds, CIFAR-10, CIFAR-100 and stl10

This also essentially makes you a complete master when it comes to handling image data most of us probably know how to handle and store numerical and categorical data in csv files.

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In this video I am discussing various techniques to handle imbalanced dataset in machine learning. I also have a python code that demonstrates these different techniques. In the end there is an exercise for you to solve along with a solution link. Credit card fraud detection, cancer prediction, customer churn prediction are some of the examples where you might get an imbalanced dataset. Training a model on imbalanced dataset requires making certain adjustments otherwise the model will not perform as per your expectations.