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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
Do you actually want to implement something? There are decent explainability libraries now, e.g., [AIX360](https://aix360.mybluemix.net/), [InterpretML](https://interpret.ml/), or [captum](https://captum.ai/). Pytorch + maybe pytorch lightning + captum might be the quickest way to actually implement something like an explainable neural net yourself. Do the standard tutorials for each of them and watch a few YT videos (or follow a coursera course or something like that) about how these things work in theory and practice, and you'll get up to speed relatively quickly. You will not be able to do useful work in ML without actually learning the ropes.
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[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation
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Can you interrogate a machine learning model to find out why it gave certain predictions?
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What kind of explainability techniques exist for Reinforcement learning?
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[D] How do you choose which Black-Box Explainability method to use?
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DeepLIFT or other explainable api implementations for JAX (like captum for pytorch)?