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image processing task I did using fine-tuning, DCNNs and common data augmentation techniques. dataset consisted of 1600 x-ray images of human stomach which included 800 each of pylori(helicobacter) positive and negative. augmented the dataset using common data augmentation techniques.
Image classification using both non-DL and DL approaches. Some interesting techniques are included like SIFT-feature extraction and multiple kernel learning (MLK).
Switching from GPU to the future of Machine learning the TPU. Over 1 million images trained Resnet50 in under 20 mins compared to days or weeks on GPU and all for 0$ free on Google Colab Notebooks in Google Drive, clone repo and jump right in!!
QuickCNN is high-level library written in Python, and backed by the Keras, TensorFlow, and Scikit-learn libraries. It was developed to exercise faster experimentation with Convolutional Neural Networks(CNN). Majorly, it is intended to use the Google-Colaboratory to quickly play with the ConvNet architectures. It also allow to train on your local…
This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.