Flipboard

JULY 10, 2023

During training, the input data is intentionally corrupted by adding noise, while the target remains the original, uncorrupted data. The autoencoder learns to reconstruct the clean data from the noisy input, making it useful for image denoising and data preprocessing tasks.

Deep Learning 132

Deep Learning Deep Learning Machine Learning Machine Learning 132

AWS Machine Learning Blog

NOVEMBER 27, 2023

While this data holds valuable insights, its unstructured nature makes it difficult for AI algorithms to interpret and learn from it. According to a 2019 survey by Deloitte , only 18% of businesses reported being able to take advantage of unstructured data. Clean data is important for good model performance.

Data Preparation 87

Data Preparation AI AI Python 87

AWS Machine Learning Blog

FEBRUARY 10, 2023

Advances in neural information processing systems 32 (2019). Visualizing data using t-SNE.” He is broadly interested in Deep Learning and Natural Language Processing. He started at the NFL in February 2020 as a Data Scientist and was promoted to his current role in December 2021. He obtained his Ph.D.

ML 74

ML ML Machine Learning Machine Learning 74

Mlearning.ai

JUNE 28, 2023

I came up with an idea of a Natural Language Processing (NLP) AI program that can generate exam questions and choices about Named Entity Recognition (who, what, where, when, why). I let only the word with the pos of NOUN, VERB, ADJ, and ADV to pass through the filter and continue to the next process.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing AI 52