Remove 2019 Remove Machine Learning Remove Statistics
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Statistical Modelling vs Machine Learning

KDnuggets

At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.

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Why you should care about debugging machine learning models

O'Reilly on Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. There are several known attacks against machine learning models that can lead to altered, harmful model outcomes or to exposure of sensitive training data. [8] 2] The Security of Machine Learning. [3]

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5 key areas for tech leaders to watch in 2020

O'Reilly on Data

This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%.

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What you need to know about product management for AI

O'Reilly on Data

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

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Analytics Insights and Careers at the Speed of Data

Rocket-Powered Data Science

One CIO said it this way , “If CIOs invested in machine learning three years ago, they would have wasted their money. This article quotes an older market projection (from 2019) , which estimated “the global industrial IoT market could reach $14.2 But if they wait another three years, they will never catch up.”

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Adding Common Sense to Machine Learning with TensorFlow Lattice

The Unofficial Google Data Science Blog

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you.

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The Power of Graph Databases, Linked Data, and Graph Algorithms

Rocket-Powered Data Science

In 2019, I was asked to write the Foreword for the book “ Graph Algorithms: Practical Examples in Apache Spark and Neo4j “ , by Mark Needham and Amy E. Finally, in Chapter 8, the connection between graph algorithms and machine learning that was implicit throughout the book now becomes explicit.

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