2015, Database and Deep Learning - Data Science Current

The Window-Knocking Machine Test

Ines Montani

AUGUST 6, 2024

Around 2015 when deep learning was widely adopted and conversational AI became more viable, the industry got very excited about chat bots. So whenever you’re tasked with developing a system to replace and automate a human task, ask yourself: Am I building a window-knocking machine or an alarm clock?

Deep Learning

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Best 10 Free Datasets for Manufacturing [UPDATED]

Iguazio

MARCH 1, 2024

Here are 10 excellent open manufacturing datasets and data sources for manufacturing data for machine learning. The dataset’s base year is 2015 and depicts monthly growth rates. To learn more about ML and manufacturing, click here. Get the dataset here. Datasets include: Industrial Production and Capacity Utilization U.S.

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Best Machine Learning Datasets

Flipboard

JULY 31, 2023

Object detection works by using machine learning or deep learning models that learn from many examples of images with objects and their labels. In the early days of machine learning, this was often done manually, with researchers defining features (e.g., So, what does the MNIST database look like?

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Meet the winners of Phase 1 of the PREPARE Challenge

DrivenData Labs

SEPTEMBER 10, 2024

2nd Place Ishanu Chattopadhyay (University of Kentucky) 2 million synthetic patient records with 9 variables, generated using AI models trained on EHR data from the Truven Marketscan national database and University of Chicago (2012-2021). These patients, aged 60-75, were eventually diagnosed with AD/ADRD.

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You are probably doing Medical Imaging AI the wrong way.

Mlearning.ai

JUNE 16, 2023

The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. December 14, 2015. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.” [link] [4] Huh, Minyoung, Pulkit Agrawal, and Alexei A. April 14, 2015.

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sense2vec reloaded: contextually-keyed word vectors

Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. Try the new interactive demo to explore similarities and compare them between 2015 and 2019 sense2vec (Trask et. Interestingly, “to ghost” wasn’t very common in 2015.

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Best Machine Learning Frameworks for ML Experts in 2023

Pickl AI

JANUARY 23, 2023

It is mainly used for deep learning applications. PyTorch PyTorch is a popular, open-source, and lightweight machine learning and deep learning framework built on the Lua-based scientific computing framework for machine learning and deep learning algorithms. It also allows distributed training.

Machine Learning

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How to See Like a Machine

Mlearning.ai

JUNE 5, 2023

Many Libraries: Python has many libraries and frameworks (We will be looking some of them below) that provide ready-made solutions for common computer vision tasks, such as image processing, face detection, object recognition, and deep learning. TensorFlow An open-source framework for machine learning and deep learning.

Deep Learning

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. per diluted share, for the year ended December 31, 2015. per diluted share, for the year ended December 31, 2015.

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Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

JANUARY 13, 2023

This dataset comprises a multi-center critical care database collected from over 200 hospitals, which makes it ideal to test our FL experiments. We used the eICU Collaborative Research Database , a multi-center intensive care unit (ICU) database, comprising 200,859 patient unit encounters for 139,367 unique patients.

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. per diluted share, for the year ended December 31, 2015. per diluted share, for the year ended December 31, 2015.

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Comcast’s data-centric approach to speech interfaces

Snorkel AI

FEBRUARY 13, 2023

The voice remote was launched for Comcast in 2015. Given that many of our queries are short, domain experts are able to write rules in template form that allow us to capture a number of the intents even without deep learning. And finally, also, AI/ML innovation and educational efforts.

Machine Learning

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Comcast’s data-centric approach to speech interfaces

Snorkel AI

FEBRUARY 13, 2023

The voice remote was launched for Comcast in 2015. Given that many of our queries are short, domain experts are able to write rules in template form that allow us to capture a number of the intents even without deep learning. And finally, also, AI/ML innovation and educational efforts.

Machine Learning

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The Evolution of Tabular Data: From Analysis to AI

Towards AI

AUGUST 11, 2023

It encompasses everything from CSV files and spreadsheets to relational databases. Tabular data has been around for decades and is one of the most common data types used in data analysis and machine learning. The synthetic datasets were created using a deep-learning generative network called CTGAN.[3]

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Multi-Modal Methods: Visual Speech Recognition (Lip Reading)

ML Review

MAY 3, 2018

Much the same way we iterate, link and update concepts through whatever modality of input our brain takes — multi-modal approaches in deep learning are coming to the fore. While an oversimplification, the generalisability of current deep learning approaches is impressive.

Deep Learning

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 23, 2024

Agent Creator is a versatile extension to the SnapLogic platform that is compatible with modern databases, APIs, and even legacy mainframe systems, fostering seamless integration across various data environments. The resulting vectors are stored in OpenSearch Service databases for efficient retrieval and querying.

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Data Science Current

The Window-Knocking Machine Test

Best 10 Free Datasets for Manufacturing [UPDATED]

Webinars

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Best Machine Learning Datasets

Webinars

Meet the winners of Phase 1 of the PREPARE Challenge

You are probably doing Medical Imaging AI the wrong way.

sense2vec reloaded: contextually-keyed word vectors

Best Machine Learning Frameworks for ML Experts in 2023

How to See Like a Machine

Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

Comcast’s data-centric approach to speech interfaces

Comcast’s data-centric approach to speech interfaces

The Evolution of Tabular Data: From Analysis to AI

Multi-Modal Methods: Visual Speech Recognition (Lip Reading)

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

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