2014 ML Support Vector Machines 
Mlearning.ai
APRIL 8, 2023
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.
Becoming Human
MARCH 16, 2023
Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. AI drug discovery is exploding.
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ML Review
MARCH 5, 2019
Crafting a dataset The number of papers added to ArXiv per month since 2014. As a starting point for our lofty goal, we used the arxiv-sanity code base (created by Andrej Karpathy) to collect ~50,000 papers from the ArXiv API released from 2014 onwards and which were in the fields of cs. Every month except January.
Mlearning.ai
FEBRUARY 15, 2023
Uysal and Gunal, 2014). The accuracy of the ML model indicates how many times it was correct overall. Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm. Introduction In natural language processing, text categorization tasks are common (NLP). Dönicke, T., Cambridge: MIT Press.
Mlearning.ai
JUNE 8, 2023
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.
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