Classifiers

CIFAR10 Classifier: PyTorch + Ray Tune Edition Objective Explore Ray Tune and in the process attempt to to find a better set of hyperparameters that can beat the best test acurracy: [Read More]

CIFAR10 Classfier: TensorFlow + Optuna Edition Our objective is similar to the Keras-Tuner and Ray Tune notebooks: Explore Optuna optimization library for hyperparam tuning Find out if we can beat the test accuracy of a hand tuned model 69. [Read More]

CIFAR10 Classification: TensorFlow + Ray Tune Edition In this notebook, we turn to hyperparameter tuning using Ray Tune. Our objective is to find better hyperparams and network architecture. Although we will not spend too much time on network architecture, you probably will get enough information to try on your own. [Read More]

This notebook is part of CIFAR-10 Classifiers post, which covers building classifiers using TensorFlow, PyTorch, PyTorch Lightning etc. Part 1: Tensorflow | Part 3: PyTorch CIFAR 10 Classifier: Pytorch Lightning Edition We use Pytorch Lightning in this notebook, it abstracts away all the boiler plate code that we need to add to every pytorch training workflow. [Read More]

Part 1: Tensorflow This notebook is part of CIFAR-10 Classifiers post. In that post, I cover building classifiers using Deep Learning frameworks such as TensorFlow, PyTorch, PyTorch Lightning and making use of several hyperparameter optimization libraries. [Read More]

This notebook is part of CIFAR-10 Classifiers post, which covers building classifiers using TensorFlow, PyTorch, PyTorch Lightning etc. CIFAR-10 Classifier: TensorFlow 2.x Edition Objective Build a simple CIFAR10 classification model that runs on GPU using following: [Read More]