Archive for December 2017

MNIST with Kaggle Kernel



Today, I’d like to share a quick post. As you already know, Kaggle is the place to study and learn machine learning. Kaggle’s users share their solutions and insights for several problems in ML. How can they share so many information in an easy and practical way? Users share their knowledge through kernels, how it looks like?┬áLet’s take a look?

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MNIST – Regularized Logistic Regression

Hello guys


Sometimes when we train our algorithm, it becomes too specific to our dataset which is not good. Why? Because the algorithm must be able to classify correctly data never seem before too. So today, I’ll show you a way to try to improve the accuracy of our algorithm.

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Kaggle MNIST competition


Last post we classified MNIST using our classifier. I was wondering how we could evaluate our algorithm with “real” data. What if we could evaluate it against others algorithms from other people?

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