Remove 2018 Remove ML Remove Support Vector Machines
article thumbnail

How To Improve Machine Learning Model Accuracy

DagsHub

In 2018, there were extensive news reports that an Uber self-driving car made an accident with a pedestrian in Tempe, Arizona. The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand. These are: 1.

article thumbnail

NLP-Powered Data Extraction for SLRs and Meta-Analyses

Towards AI

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. Over the past several years, researchers have increasingly attempted to improve the data extraction process through various ML techniques. This study by Bui et al.

professionals

Sign Up for our Newsletter

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

Trending Sources

article thumbnail

How HSR.health is limiting risks of disease spillover from animals to humans using Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Incorporating ML with geospatial data enhances the capability to detect anomalies and unusual patterns systematically, which is essential for early warning systems.

ML 110
article thumbnail

From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms.

article thumbnail

What a data scientist should know about machine learning kernels?

Mlearning.ai

Before we discuss the above related to kernels in machine learning, let’s first go over a few basic concepts: Support Vector Machine , S upport Vectors and Linearly vs. Non-linearly Separable Data. The linear kernel is ideal for linear problems, such as logistic regression or support vector machines ( SVMs ).

article thumbnail

AI Distillery (Part 1): A bird’s eye view of AI research

ML Review

In 2018, over 1000 papers have been released on ArXiv per month in the above areas. Instead, we manually defined the important set of concepts from the larger set of most common n-grams — “recurrent neural networks”, “support vector machine”, etc. Every month except January. Over 2000 papers were released in November.

AI 52
article thumbnail

Embeddings in Machine Learning

Mlearning.ai

Netflix-style if-you-like-these-movies-you’ll-like-this-one-too) All kinds of search Text search (like Google Search) Image search (like Google Reverse Image Search) Chatbots and question-answering systems Data preprocessing (preparing data to be fed into a machine learning model) One-shot/zero-shot learning (i.e.