Tag: machine learning

Machine Learning: The Why, When and How

Why Machine Learning?

Why machine learning? At its simplest, machine learning (ML) uses mathematical models to analyze large volumes of data, identify patterns and make decisions. ML models can imitate human behavior to predict outcomes, such as those used for language translation, chatbot…

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Putting ML Prototypes Into Production Using TensorFlow Extended (TFX)

Introduction Machine learning projects start by building a proof-of-concept or a prototype. This entails choosing the right dataset (features), the appropriate ML algorithm/model and the hyper-parameters for that algorithm. As a result of a POC, we would have a trained…

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Five Ways Your Machine Learning Model Is like Cookie Monster

At the highest, simplest level, a machine learning (ML) model is an algorithm that ingests data and spits out insights, predictions or recommendations. It has two important phases—first you have to train your model (training) then you let the model…

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Heart Disease Prediction Using Keras Deep Learning

Heart disease covers a range of different conditions that could affect your heart. It is one of the most complex diseases to predict given the number of potential factors in your body that can lead to it. Identifying and predicting…

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Deep Learning: Techniques to Avoid Overfitting and Underfitting

Machine Learning is all about striking the right balance between optimization and generalization. Optimization means tuning your model to squeeze out every bit of performance from it. Generalization refers to making your model generic enough so that it can perform…

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Three Announcements from Oracle OpenWorld You Probably Didn’t Hear

The buzz from OpenWorld this year was the permanently free Oracle Cloud offering, the new Exadata X8M (reportedly capable of 12 million I/O per second!), Oracle Autonomous Cloud at Customer, and the new Blockchain table. Here are three interesting or…

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Report: Data Warehouses Are Heading To The Cloud

Unisphere Research Report uncovers the trends that are driving analytics workloads to the cloud, including real-time analytics and machine learning.   Data strategies are now commonplace, machine learning adoption has doubled in a year, and data warehouses are still a go-to…

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Making a business case for Machine Learning

The first step to kick off a Machine Learning (ML) project is to have a written proposition for the business problem, and second, to frame the ML problem. Before even discussing an ML method, it is necessary first to understand…

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An overview of best practices for implementing ML systems – Part 1

In this series of blog posts, we will recommend some best practices identified from our own failures and successes throughout our time implementing machine learning (ML) systems. We won’t discuss ML techniques here, but instead, provide an upper-level overview of…

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Datascape Podcast Ep 34 – Recapping Microsoft Build 2019 with Warner Chaves

Today on the Datascape Podcast we are joined by Warner Chaves to discuss the most exciting announcements from the recent Microsoft Build 2019 Conference! We run through a host of different products and services with Warner, hand-picking the most most…

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