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    What Is Reinforcement Learning and How Is It Used?

    When you do something well, you’re rewarded. This simple principle has guided humans since the beginning of time, and now, more than ever before, it is the key principle...

    A Detailed Guide To Transfer Learning and How It Works

    For data science teams working with inadequate data or too much data and not enough time or resources to process it, transfer learning can represent...

    Explaining black-box models using attribute importance, PDPs, and LIME

    In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights...

    The Future of Data Science - Mining GTC 2021 for Trends

    Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. I enjoyed mining this year’s...

    Trending Toward Concept Building - A Review of Model Interpretability for Deep Neural Networks

    We are at an interesting time in our industry when it comes to validating models - a crossroads of sorts when you think about it. There is an...

    The Curse of Dimensionality

    Guest Post by Bill Shannon, Founder and Managing Partner of BioRankings Danger of Big Data Big data is the rage. This could be lots of rows (samples)...

    Evaluating Generative Adversarial Networks (GANs)

    This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. If you are...

    Data Drift Detection for Image Classifiers

    This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in...

    Natural Language Processing in Python using spaCy: An Introduction

    This article provides a brief introduction to natural language using spaCy and related libraries in Python. The complementary Domino project is also...

    Deep Reinforcement Learning

    This article provides an excerpt "Deep Reinforcement Learning" from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The article...

    A Practitioner's Guide to Deep Learning with Ludwig

    Joshua Poduska provides a distilled overview of Ludwig including when to use Ludwig’s command-line syntax and when to use its Python API. ...

    Highlights from the Maryland Data Science Conference: Deep Learning on Imagery and Text

    Niels Kasch, cofounder of Miner & Kasch, an AI and Data Science consulting firm, provides insight from a deep learning session that occurred at the ...

    Themes and Conferences per Pacoid, Episode 7

    Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Introduction Welcome...

    Themes and Conferences per Pacoid, Episode 3

    Paco Nathan‘s column covers themes that include open source, "intelligence is a team sport", and "implications of massive latent hardware". ...