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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.
The objective of the project is - to use Fashion-MNIST data set to identify (predict) different fashion products(articles) from the given images using Machine Learning.
We will be following the below steps to solve this problem:
Importing the libraries
Using some pre-defined utility functions
Loading the data
Cleaning the data
Dividing the dataset into training and test dataset using train_test_split in the ratio 85:15
Training several models and analyzing their performance to select a model
Use dimensionality reduction to improve the ‘training’,
‘fine-tuning’ and ‘prediction’ time.
Fine-tuning the model by finding the best hyper-parameters and features
Evaluating selected model using test dataset
Acknowledgements
Cloudxlab is using this “Fashion MNIST” problem for its machine learning learners for learning and practicing. Fashion-MNIST dataset is a collection of fashion article's images provided by Zalando . We thank Zalando Research for hosting the dataset.
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