Classifying images of everyday objects using a neural network 👻

Classifying images of everyday objects using a neural network

The ability to try many different neural network architectures to address a problem is what makes deep learning really powerful, especially compared to shallow learning techniques like linear regression, logistic regression etc. In this assignment, you will: Explore the CIFAR10 dataset: https://www.cs.toronto.edu/~kriz/cifar.html Set up a training pipeline to train a neural network on a GPU Experiment with different network architectures & hyperparameters As you go through this notebook, you will find a ??? in certain places. Your job is to replace the ??? with appropriate code or values, to ensure that the notebook runs properly end-to-end. Try to experiment with different network structures and hypeparameters to get the lowest loss. You might find these notebooks useful for reference, as you work through this notebook: https://jovian.ml/aakashns/04-feedforward-nn https://jovian.ml/aakashns/fashion-feedforward-minimal

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