The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available.

N.B.: Blue maps are inputs, and cyan maps are outputs. N.B.: Blue maps are inputs, and cyan maps are outputs. N.B.: Blue maps are inputs, and cyan maps are outputs. The animations will be output to the gif directory. Individual animation steps will be output in PDF format to the pdf directory and in PNG format to the png directory.

The real value of machine learning models lies in their usability. If the model is not properly deployed, used, and continuously updated through cycles of customer feedback, it is doomed to stay in a GitHub repository, never reaching its actual potential. In this article, we will learn how to deploy a Spotify Recommendation Model in Flask in a few simple steps. The application we will deploy is stored in a recommendation_app folder. In the root directory, we have the wsgi.py

Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting and anomaly detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets. The table below provides a visual overview of how Merlion's key features compare to other libraries for time series anomaly detection and/or forecasting.

In this article, I will share how to create a custom component in rasa to make entity extraction more robust to typos. More specifically, we will use the fuzzywuzzy library to do fuzzy string matching to autocorrect an entity based on its similarity score. The code to reproduce the bot described in this article can be found here. Suppose the bot is expected to extract entities representing a country from an utterance and normalize them so some canonical form. This can be done with rasa's synonyms feature: Therefore, an utterance like "I am from the united states" will be processed by the NLU pipeline as: However, if the user made a typo e.g.

Like any other language or tool, Python has some best practices to follow before, during, and after the process of writing your code. These make the code readable and create a standard across the industry. Other developers working on the project should be able to read and understand your code. We have listed out a few of these for you to follow and write cleaner and more professional code. Do you follow any of these?

The first thing you'll want to do is install PlatformIO. Now, create your project's root directory. This directory should also contain sub-directories for src, lib, and include. Within your project's root directory, create a file named platformio.ini. This file will contain all of the information needed for PlatformIO to initialize your development environment.

Editor's note: This post covers Favio's selections for the top 7 Python libraries of 2018. Tomorrow's post will cover his top 7 R packages of the year. If you follow me, you know that this year I started a series called Weekly Digest for Data Science and AI: Python & R, where I highlighted the best libraries, repos, packages, and tools that help us be better data scientists for all kinds of tasks. The great folks at Heartbeat sponsored a lot of these digests, and they asked me to create a list of the best of the best--those libraries that really changed or improved the way we worked this year (and beyond). Disclaimer: This list is based on the libraries and packages I reviewed in my personal newsletter.

If you follow me, you know that this year I started a series called Weekly Digest for Data Science and AI: Python & R, where I highlighted the best libraries, repos, packages, and tools that help us be better data scientists for all kinds of tasks. The great folks at Heartbeat sponsored a lot of these digests, and they asked me to create a list of the best of the best--those libraries that really changed or improved the way we worked this year (and beyond). AdaNet is a lightweight and scalable TensorFlow AutoML framework for training and deploying adaptive neural networks using the AdaNet algorithm [Cortes et al. AdaNet combines several learned subnetworks in order to mitigate the complexity inherent in designing effective neural networks. This package will help you selecting optimal neural network architectures, implementing an adaptive algorithm for learning a neural architecture as an ensemble of subnetworks.

XLearning is a convenient and efficient scheduling platform combined with the big data and artificial intelligence, support for a variety of machine learning, deep learning frameworks. XLearning has the satisfactory scalability and compatibility. Besides the distributed mode of TensorFlow and MXNet frameworks, XLearning supports the standalone mode of all deep learning frameworks such as Caffe, Theano, PyTorch. Moreover, XLearning allows the custom versions and multi-version of frameworks flexibly. XLearning is enable to specify the input strategy for the input data --input by setting the --input-strategy parameter or xlearning.input.strategy