Remove 2018 Remove Deep Learning Remove Experimentation Remove Optimization
article thumbnail

ChatGPT, the rise of generative AI

CIO Business Intelligence

A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. ChatGPT was trained with 175 billion parameters; for comparison, GPT-2 was 1.5B (2019), Google’s LaMBDA was 137B (2021), and Google’s BERT was 0.3B (2018). What is ChatGPT? ChatGPT is a product of OpenAI.

article thumbnail

Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

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 excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Themes and Conferences per Pacoid, Episode 11

Domino Data Lab

SQL optimization provides helpful analogies, given how SQL queries get translated into query graphs internally , then the real smarts of a SQL engine work over that graph. Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. AutoPandas: Origins.

Metadata 105
article thumbnail

Topics to watch at the Strata Data Conference in New York 2019

O'Reilly on Data

So, we used a form of the Term Frequency-Inverse Document Frequency (TF/IDF) technique to identify and rank the top terms in this year’s Strata NY proposal topics—as well as those for 2018, 2017, and 2016. 2) is unchanged from Strata NY 2018, it’s up three places from Strata NY 2017—and eight places relative to 2016. 221) to 2019 (No.

IoT 20
article thumbnail

Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

2018-06-21). For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.