Mlearning.ai

FEBRUARY 28, 2023

In 2018–2019, while new car sales were recorded at 3.6 Before building a model, I have to encode categorical features. This is a clear indication that a good model is formed to explain variance in the price of used cars up to 87%. million units, around 4 million second-hand cars were bought and sold. MAPE of 43.55

Python 52

Python Data Analysis Explainability Categorization 52

Snorkel AI

MAY 25, 2023

As the original researchers explained, “the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.” From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models. What are large language model capabilities?

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

As the original researchers explained, “the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.” From 2018 to the modern day, NLP researchers have engaged in a steady march toward ever-larger models. What are large language model capabilities?

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Mlearning.ai

JULY 2, 2023

The Art of Forecasting in the Retail Industry Part I : Exploratory Data Analysis & Time Series Analysis In this article, I will conduct exploratory data analysis and time series analysis using a dataset consisting of product sales in different categories from a store in the US between 2015 and 2018. df.loc[(df["Postal Code"].isnull()),

Categorization 52

Categorization Data Analysis Data Science ML 52

Snorkel AI

MARCH 1, 2023

This can make it challenging for businesses to explain or justify their decisions to customers or regulators. 2018) This paper introduced BERT, a language model that uses bidirectional context to better understand the meaning of words in a sentence. Interpretability Foundation models are often described as “black boxes.”

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Explosion

MAY 17, 2023

In 2014 I started working on spaCy , and here’s an excerpt of how I explained the motivation for the library: Computers don’t understand text. We want to aggregate it, link it, filter it, categorize it, generate it and correct it. This is unfortunate, because that’s what the web almost entirely consists of.

LLM 135

LLM NLP Large Language Models OpenAI 135

AWS Machine Learning Blog

FEBRUARY 8, 2023

At a high level, these metrics are categorized into three classes: invocation metrics, latency metrics, and utilization metrics. This post has reviewed each of these steps and explained the approach you should take at each step. The following diagram illustrates the application and scaling setup in SageMaker.

Machine Learning 93

Machine Learning Auto-complete ML Natural Language Processing 93

Snorkel AI

MAY 12, 2023

Recent research published in the Harvard Business Review in 2018 suggests that nearly $31.5 Now I’m going to pass it on to Bayan, who’s going to focus on another very important aspect of data, which is variability and explainability. That is a staggering statistic—50 to 80 percent of the end-to-end time is in data prep.

Machine Learning 52

Machine Learning Data Scientist Data Science ML 52

Snorkel AI

MAY 12, 2023

Recent research published in the Harvard Business Review in 2018 suggests that nearly $31.5 Now I’m going to pass it on to Bayan, who’s going to focus on another very important aspect of data, which is variability and explainability. That is a staggering statistic—50 to 80 percent of the end-to-end time is in data prep.

Machine Learning 52

Machine Learning Data Scientist Data Science ML 52

Explosion

MARCH 17, 2019

In this post, we highlight some of the things we’re especially pleased with, and explain some of the most challenging parts of preparing this big release. Language model pretraining By far the biggest news in NLP research over 2018 was the success of language model pretraining. Version 2.1 Devlin et al. Even more recently, Li et al.

NLP 52

NLP Python Neural Network Natural Language Processing 52

PyImageSearch

NOVEMBER 13, 2023

For more information, including a worked example of how to compute mAP, please see Hui (2018). All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. The (Faster) R-CNN Architecture In this section, we’ll review the Faster R-CNN architecture. That’s not the case.

Convolutional Neural Networks 119

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 119

Mlearning.ai

MARCH 24, 2023

Photo by Guillaume Périgois on Unsplash EU AI Act: History and Timeline 2018 : EU Commission starts pilot project on ‘Explainable AI’. Paul Nemitz , a key contributor to GDPR and now actively involved in forming the EU AI Act, explains how unregulated AI can be a threat to Democracy.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Categorization Explainability 52

AWS Machine Learning Blog

AUGUST 7, 2023

It will explain the thought process and experimentation behind the solution, including the model training and development process. Part 2 will delve into the productionized solution, explaining the design decisions, data flow, and illustration of the model training and deployment architecture. Attention is All You Need” Radford et al.,

Large Language Models 87

Large Language Models LLM BERT NLP 87

IBM Journey to AI blog

FEBRUARY 9, 2024

Prior to the current hype cycle, generative machine learning tools like the “Smart Compose” feature rolled out by Google in 2018 weren’t heralded as a paradigm shift, despite being harbingers of today’s text generating services.

AI 316

AI AI Artificial Intelligence Artificial Intelligence 316