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algorithms-in-python

autoencoder-chromatogram-enhancement

Implementation of a denoising Autoencoder to reduce noise in chromatograms.

ddpg-optimal-scouting-runs

Deep deterministic policy gradient algorithm for the selection of optimal gradient scouting runs

ddqn-isocratic-scouting-runs

Implementation of a double deep Q-learning algorithm to select optimal isocratic scouting runs in liquid chromatography.

deep-learning-peak-detection

Implementation of a deep learning model for finding peaks in RPLC output

gans-for-molecules

gcn-retention-time-predictions

Implementation of graph convolutional networks to predict chromatographic retention times.

kaggle-landmark-retrieval-and-recognition-2020

kaggle-prostate-cancer-grade-assessment

Classification of the severity of prostate cancer from prostate biopsy samples. A preprocessing step aims to extract the most relevant information of the high resolution images, which is then input into a deep neural network.

kaggle-siim-isic-melanoma-classification

Melanoma classification based on raw image data using deep neural networks. TFRecordDataset is used to optimize the input pipeline.

kaggle-tweet-sentiment-extraction

Aims to extract sentiments from paragraphs using advanced language models like XLNet and RoBERTa .

keras-weighted-multilabel-binary-crossentropy

Weighted Binary-Crossentropy for multilabel classification in Keras

molcraft

Generative Deep Learning for Molecules

molgraph

Graph neural networks for molecular machine learning. Implemented and compatible with TensorFlow and Keras.

reinforceable

Deep reinforcement learning implementations using TensorFlow and TensorFlow probability.

single-peak-integration-using-dl