Damage to the meniscus is a physically limiting injury that can lead to further medical complications. Automatically classifying this damage at the time of an MRI scan would allow quicker and more accurate diagnosis. Jennie Wang, Valentina Pedoia, Berk Norman, and Yulia Tell offer an overview of their classification system built with 3D convolutional neural networks using BigDL on Apache Spark.

Ash Munshi shares techniques for labeling big data apps using runtime measurements of CPU, memory, I/O, and network and details a deep neural network to help operators understand the types of apps running on the cluster and better predict runtimes, tune resource utilization, and increase efficiency. These methods are new and are the first approach to classify multivariate time series.

Mohamed AbdelHady and Zoran Dzunic demonstrate how to build a domain-specific entity extraction system from unstructured text using deep learning. In the model, domain-specific word embedding vectors are trained on a Spark cluster using millions of PubMed abstracts and then used as features to train a LSTM recurrent neural network for entity extraction.

Time series forecasting and anomaly detection is of utmost importance at Uber. However, the scale of the problem, the need for speed, and the importance of accuracy make anomaly detection a challenging data science problem. Andrea Pasqua and Anny Chen explain how the use of recurrent neural networks is allowing Uber to meet this challenge.

At AIweirdness.com Janelle Shane posts the results of neural network experiments gone delightfully wrong. But machine learning mistakes can also be very embarrassing or even dangerous. Using silly datasets as examples, Janelle talks about some ways that algorithms fail.

Word vector embeddings are everywhere, but relatively few understand how they produce their remarkable results. Patrick Harrison opens up the black box of a popular word embedding algorithm and walks you through how it works its magic. Patrick also covers core neural network concepts, including hidden layers, loss gradients, backpropagation, and more.

Sports analytics today is more than a matter of analyzing box scores and play-by-play statistics. Faced with detailed on-field or on-court data from every game, sports teams face challenges in data management, data engineering, and analytics. Thomas Miller details the challenges faced by a Major League Baseball team as it sought competitive advantage through data science and deep learning.

Burkhard Noltensmeier shares his experience developing a Microservice data aquisition platform on Kubernetes. The Platform consists of Edge Devices with to import Machine Data from factory floor or bu …