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Your help in Fashion : 7 layer CNN at your service (~92% accurate)

Hey Readers! Welcome to yet another post where I play with a self designed neural network. This CNN would be tackling a variant of classical MNIST known as Fashion MNIST dataset  . Before we start exploring what is the approach for this dataset, let's first checkout what this dataset really is. Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. The original MNIST dataset contains a lot of handwritten digits. Members of the AI/ML/Data Science community love this dataset and use it as a benchmark to validate their algorithms. In fact, MNIST is often the first dataset researchers try. ...

Tackling MNIST Dataset : Deep Convolutional Neural Network 99.571% accurate

Hey there! My dear readers.  Today this kernel review is going to be about the famous MNIST data-set, one of the most famous introductory datasets which we encounter along side Iris dataset and the titanic survival challenge data sets.  Since it is a competition kernel, I have decided not to make it public. (yet) Also,  if you want to try your hands at the challenge itself, then you can find the challenge page here :  Note: This kernel has been largely focused on network modelling rather than Exploratory Data Analysis because it's simple, classic stuff. Still, I will try my best to explain that stuff here. Exploratory Data Analysis First indication of a great dataset is the face that it gives all the mentioned labels equal rows in it and this one doesn't disappoint. This data set maintains a fair 4000+ entries per label which actually is a great statistic for a good dataset. And the next thing one needs to know is how...

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