An interactive framework to visualize and analyze your AutoML process in real-time.
A Rust implementation of fANOVA (functional analysis of variance)
Hyperparameter tuning analysis based on HpTuning project results.
A PyTorch project exploring CNN model efficiency, featuring from-scratch implementations of MobileNetV1 and MobileNetV2. Demonstrates model compression by using Knowledge Distillation to train a lightweight MobileNetV2 (student) from a ResNet-18 (teacher).
I have trained two different CNN models for binary image classification to see which architecture has better accuracy, takes less time in training, how hyperparamters affect training and how many epochs do each of them need. I achieved 96% accuracy on the best model.
This repository is the code basis for the paper intitled "Exploring the Intricacies of Neural Network Optimization"
🚀 Explore CNN efficiency with PyTorch implementations of MobileNetV1 and V2, featuring knowledge distillation for lightweight model training on CIFAR-10.
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