Remove Deep Learning Remove Machine Learning Remove Metrics
article thumbnail

KL Divergence: The Information Theory Metric that Revolutionized Machine Learning

Analytics Vidhya

Introduction Few concepts in mathematics and information theory have profoundly impacted modern machine learning and artificial intelligence, such as the Kullback-Leibler (KL) divergence.

article thumbnail

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. If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. 2] The Security of Machine Learning. [3]

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Meta-Learning For Better Machine Learning

Rocket-Powered Data Science

So, you start by assuming a value for k and making random assumptions about the cluster means, and then iterate until you find the optimal set of clusters, based upon some evaluation metric. The above example (clustering) is taken from unsupervised machine learning (where there are no labels on the training data).

article thumbnail

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.

article thumbnail

Top 14 Must-Read Data Science Books You Need On Your Desk

datapine

2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Best for: This best data science book is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field. 4) “Machine Learning Yearning” by Andrew Ng.

article thumbnail

What are model governance and model operations?

O'Reilly on Data

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

Modeling 256
article thumbnail

Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

2) MLOps became the expected norm in machine learning and data science projects. 3) Concept drift by COVID – as mentioned above, concept drift is being addressed in machine learning and data science projects by MLOps, but concept drift so much bigger than MLOps. And the goodness doesn’t stop there.