Tag: Deep Learning

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 talks for trends that foreshadow...

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...

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...

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 interested in a tutorial...

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 production....

Deep Reinforcement Learning

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

Deep Learning Illustrated: Building Natural Language Processing Models

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt "Natural Language Processing" from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The...

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. Introduction New tools are...

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 Maryland...

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". Introduction Welcome to our monthly series...

Benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST

In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, writes about benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST....

Avoiding a Data Science Hype Bubble

In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, advocates for a common taxonomy of terms within the data science industry. The proposed...

Taking the Course: Practical Deep Learning for Coders

In this blog post, Lisa Green, Head of Domino for Good, describes the content, value, and experience of taking Lesson 1 of the Practical Deep Learning...

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