Was 2020 really bad year?

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All around tells that 2020 was worst year ever. But cmon, look at hilarious progress in AI since this year. I read singularity reddit whole year, and damm breakthroughs comes day by day. What will in next years ? Several breakthroughs per day, or even ten per day ? I think 2020 was a great start of unimaginable drastic decade, that will change truly every aspect of our lives. submitted… Read More »Was 2020 really bad year?

Predicting soccer goals in near real time using computer vision

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In a soccer game, fans get excited seeing a player sprint down the sideline during a counterattack or when a team is controlling the ball in the 18-yard box because those actions could lead to goals. However, it is difficult for human eyes to fully capture such fast movements, let alone predict goals. With machine learning (ML), we can incorporate more fine-grained information at the pixel level to develop a… Read More »Predicting soccer goals in near real time using computer vision

Getting started with the Amazon Kendra Google Drive connector

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Amazon Kendra is a highly accurate and easy-to-use intelligent search service powered by machine learning (ML). To simplify the process of connecting data sources to your index, Amazon Kendra offers several native data source connectors to help get your documents easily ingested. For many organizations, Google Drive is a core part of their productivity suite, and often contains important documents and presentations. In this post, we illustrate how you can… Read More »Getting started with the Amazon Kendra Google Drive connector

Incremental learning: Optimizing search relevance at scale using machine learning

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Amazon Kendra is releasing incremental learning to automatically improve search relevance and make sure you can continuously find the information you’re looking for, particularly when search patterns and document trends change over time. Data proliferation is real, and it’s growing. In fact, International Data Corporation (IDC) predicts that 80% of all data will be unstructured by 2025. However, mining data for accurate answers continues to be a challenge for many… Read More »Incremental learning: Optimizing search relevance at scale using machine learning

Autoencoder Feature Extraction for Regression

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Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An autoencoder is composed of encoder and a decoder sub-models. The encoder compresses the input and the decoder attempts to recreate the input from the compressed version provided by the encoder. After training, the encoder model is saved and the decoder is discarded. The encoder can then be used as a… Read More »Autoencoder Feature Extraction for Regression