Deep Learning / machine learning pipelines

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4317 (+0) ⭐

An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.

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2829 (+0) ⭐

Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft

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Deep Learning Pipelines for Apache Spark

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DeepMining πŸ‚
3 (+0) ⭐

Auto-tuning Machine Learning Pipelines

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photonai 🌿
54 (+0) ⭐

PHOTONAI is a high level python API for designing and optimizing machine learning pipelines.

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OOPS_ML 🌿
5 (+0) ⭐

An object oriented approach to develop ETL pipelines, train machine learning/deep learning models and easy inference along with API endpoints implemented using pyramid web framework with Swagger UI API documentation.

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HybridTox2D πŸ‚
6 (+0) ⭐

In recent times, toxicological classification of chemical compounds is considered to be a grand challenge for pharma-ceutical and environment regulators. Advancement in machine learning techniques enabled efficient toxicity predic-tion pipelines. Random forests (RF), support vector machines (SVM) and deep neural networks (DNN) are often ap-plied to model the toxic effects of chemical compounds. However, complexity-accuracy tradeoff still needs to be ac-counted in order to improve the efficiency and commercial deployment of these methods. In this study, we implement a hybrid framework consists of a shallow neural network and a decision classifier for toxicity prediction of chemicals that interrupt nuclear receptor (NR) and stress response (SR) signaling pathways. A model based on proposed hybrid framework is trained on Tox21 data using 2D chemical descriptors that are less multifarious in nature and easy to calcu-late. Our method achieved the highest accuracy of 0.847 AUC (area under the curve) using a shallow neural network with only one hidden layer consisted of 10 neurons. Furthermore, our hybrid model enabled us to elucidate the inter-pretation of most important descriptors responsible for NR and SR toxicity.

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