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Microservices Security with gRPC and Kafka

In this video, we join Nuwan Dias in securing service-to-service interactions over gRPS and Kafka.

Converting Pure Deep Learning with PyTorch to Use Lightning and Hangar

In this video, machine learning expert Eli Stevens showcases how to use open-source libraries that are available in the PyTorch ecosystem to cut down the amount of the code that you want to write.

Deploying Machine Learning Models, Part 1: saving models

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Sentiment Classification Using a Large Movie Review Dataset, Part 2

From Machine Learning with TensorFlow, Second Edition by Chris Mattmann This article covers: Building sentiment classifier using logistic regression and with softmax Measuring classification accuracy Computing ROC curve and measure classifier effectiveness Submitting your results to the Kaggle challenge for… Continue Reading →

Deep Transfer Learning for NLP with Transformers

From Transfer Learning for Natural Language Processing by Paul Azunre

In this article, we cover some representative deep transfer learning modeling architectures for NLP that rely on a recently popularized neural architecture – the transformer – for key functions.

Generating Code with Template Functions

From Domain-Specific Languages Made Easy by Meinte Boersma This article shows you how to use modern JavaScript in a smart way to comfortably implement templates for text/code generation, instead of using a template engine. The following article is a standalone… Continue Reading →

Ingesting Data

By Vlad Riscutia

This is an excerpt from chapter 2 of my book — Data Engineering on Azure — which deals with storage. In this article we’ll look at a few aspects of data ingestion: frequency and load type, and how we can handle corrupted data. We’ll use Azure Data Explorer as the storage solution, but keep in mind that the same concepts apply regardless of the data fabric used. Code samples are omitted from this article, but are available in the book. Let’s start by looking at the frequency with which we ingest data.

Monad Interfaces and Combinators

From Functional Programming in Kotlin by Marco Vermeulen, Rúnar Bjarnason, and Paul Chiusano

This article covers how monads, monad combinators, and functors work and why you should be afraid of them.

Free eBook: Graphs and network science: An Introduction

Graphs and network science: An Introduction is a free eBook with chapters selected by Tomaz Bratanic.

Applying VACUUM to Data

From Cloud Native Machine Learning by Carl Osipov

The goal of this article is to teach you the data quality criteria you should use across any machine learning project, regardless of the dataset. This means that this part deals primarily with concepts rather than code.

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