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Connor Shorten
Connor Shorten

2.1K Followers

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Aug 16

Release Notes — Nils Reimers on Cohere Search AI, Weaviate Podcast #63

Hey everyone! Thank you so much for watching the Weaviate Podcast! I am aiming to make more content around these podcasts to hopefully capture more of the value in the conversation. Here is a short description of each of the chapters discussed and their timestamps if you would like to…

Vector Database

5 min read

Release Notes — Nils Reimers on Cohere Search AI, Weaviate Podcast #63
Release Notes — Nils Reimers on Cohere Search AI, Weaviate Podcast #63
Vector Database

5 min read


Sep 27, 2022

How to add Timestamps to Data Objects in Weaviate and then Sort by Time

Hey everyone, I wanted to just quickly document this in case someone else finds themself working on a similar problem. Step 1 — Set a date property in Weaviate { "dataType": ["date"], "name": "timeEntered", "description": "The time this object entered Weaviate." } Step 2 — (If uploading in Python, timestamp…

Artificial Intelligence

2 min read

How to add Timestamps to Data Objects in Weaviate and then Sort by Time
How to add Timestamps to Data Objects in Weaviate and then Sort by Time
Artificial Intelligence

2 min read


May 27, 2022

ANN Benchmarks with Etienne Dilocker — Weaviate Podcast #16

Approximate Nearest Neighbor (ANN) search is one of the core drivers of industrial scale Vector Search [1]. This article and podcast describes techniques to understand exactly how well ANN algorithms solve this problem when benchmarked on different: Dataset Sizes (e.g. 1 million versus 1 billion vectors) Vector Dimensionality (e.g. …

Artificial Intelligence

7 min read

ANN Benchmarks with Etienne Dilocker — Weaviate podcast #16
ANN Benchmarks with Etienne Dilocker — Weaviate podcast #16
Artificial Intelligence

7 min read


Jul 15, 2021

Deep Learning with Code Data

Code languages, such as Python or Java, have become a core application area of Deep Learning. OpenAI and GitHub have recently unveiled “Copilot” and the corresponding paper describing the technology and underlying “Codex” models. Copilot is powered by taking the GPT-3 language modeling show on the road to datasets made…

Artificial Intelligence

6 min read

Deep Learning with Code Data
Deep Learning with Code Data
Artificial Intelligence

6 min read


Apr 9, 2021

AI Weekly Update Preview — April 12th, 2021

This article presents a few salient quotes from each of the papers that will be covered on the next AI Weekly Update on Henry AI Labs! Major Themes of the Latest Papers Self-Supervised Learning Vision-Language Learning Generative Modeling (mostly GANs) Meta-Learning NLP Generalization Model-based RL Code Examples (GPT-Neo, RAPIDS+Determined, PT Lightning+DeepSpeed) Meta (Ruder Newsletter, Commentary on…

Deep Learning

10 min read

AI Weekly Update Preview — April 12th, 2021
AI Weekly Update Preview — April 12th, 2021
Deep Learning

10 min read


Dec 28, 2020

AI Weekly Update — December 28th, 2020

Computer Vision, taken over by Transformers Dear Readers, Thank you for checking out the AI Weekly Update Newsletter from Henry AI Labs! This newsletter tours updates in Deep Learning and Artificial Intelligence, providing quotes and images that tell each story. I am working on publishing my first experimental paper in Contrastive Learning. More than anything else, this has…

Artificial Intelligence

10 min read

AI Weekly Update — December 28th, 2020
AI Weekly Update — December 28th, 2020
Artificial Intelligence

10 min read


Nov 27, 2020

Tokenizing CORD-19 with NLTK

Scientific overload is one of the toughest challenges facing scientists today. As Machine Learning researchers, we constantly complain about the fast pace of arxiv uploads and praise organization tools like Arxiv Sanity Preserver. The scientific response to COVID-19 is another example of information overload. The CORD-19 dataset documents over 100K…

Deep Learning

5 min read

Tokenizing CORD-19 with NLTK
Tokenizing CORD-19 with NLTK
Deep Learning

5 min read


Published in

Towards Data Science

·Oct 27, 2020

Self-Training for Natural Language Understanding

This article will explain an exciting development in Natural Language Processing. The paper presents a Semi-Supervised Learning algorithm that significantly improves RoBERTa’s performance with Self-Training. If you prefer a video explanation of the paper, please check this out! Transfer Learning has been extremely successful in Deep Learning. This describes…

Artificial Intelligence

6 min read

Self-Training for Natural Language Understanding!
Self-Training for Natural Language Understanding!
Artificial Intelligence

6 min read


Published in

Towards Data Science

·Jan 17, 2020

The Evolution of AlphaGo to MuZero

DeepMind’s MuZero algorithm reaches superhuman ability in 57 different Atari games. This article will explain the context leading up to it! — DeepMind recently released their MuZero algorithm, headlined by superhuman ability in 57 different Atari games. Reinforcement Learning agents that can play Atari games are interesting because, in addition to a visually complex state space, agents playing Atari games don’t have a perfect simulator they can use for planning as in…

Artificial Intelligence

8 min read

The Evolution of AlphaGo to MuZero
The Evolution of AlphaGo to MuZero
Artificial Intelligence

8 min read


Published in

Towards Data Science

·Dec 17, 2019

StyleGAN2

This article explores changes made in StyleGAN2 such as weight demodulation, path length regularization and removing progressive growing! — The first version of the StyleGAN architecture yielded incredibly impressive results on the facial image dataset known as Flicker-Faces-HQ (FFHQ). The most impressive characteristic of these results, compared to early iterations of GANs such as Conditional GANs or DCGANs, is the high resolution (1024²) of the generated images. In addition…

Deep Learning

8 min read

StyleGAN2
StyleGAN2
Deep Learning

8 min read

Connor Shorten

Connor Shorten

2.1K Followers

Check out my Deep Learning YouTube Channel! https://www.youtube.com/channel/UCHB9VepY6kYvZjj0Bgxnpbw

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