{"id":25049,"date":"2021-04-15T23:10:05","date_gmt":"2021-04-16T03:10:05","guid":{"rendered":"http:\/\/bangla.salearningschool.com\/recent-posts\/dietary-patterns-and-disease-machine-learning-deep-learning-statistical-methods-root\/"},"modified":"2021-04-15T23:10:05","modified_gmt":"2021-04-16T03:10:05","slug":"dietary-patterns-and-disease-machine-learning-deep-learning-statistical-methods-root","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=25049","title":{"rendered":"Dietary Patterns and Disease: Machine Learning, Deep Learning, Statistical Methods #Root"},"content":{"rendered":"

Dietary Patterns and Disease: Machine Learning, Deep Learning, Statistical Methods
\n—-
\nFirst the unusual one: Deep Learning and Tabular Data
\n"Tabular data is the most commonly used type of data in industry, but deep learning on tabular data receives far less attention than deep learning for computer vision and natural language processing"
\nhttps:\/\/www.fast.ai\/2018\/04\/29\/categorical-embeddings\/<\/a><\/p>\n

2nd the unrelated one (or the very related one):
\nFactor analysis in Excel tutorial: https:\/\/help.xlstat.com\/customer\/en\/portal\/articles\/2062221-factor-analysis-in-excel-tutorial<\/a>
\nHowever, you can apply Factor Analysis on dataset to find associations between target and source variables.
\nPCA also works for that. The tool above also has PCA features.
\nFor sure, Python, R or similar will have the features to apply PCA and Factor Analysis on datasets<\/p>\n

A comparison of statistical and machine-learning techniques in evaluating the association between dietary patterns and 10-year cardiometabolic risk (2002-2012): the ATTICA study.
\nhttps:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29789037<\/a><\/p>\n

Dietary patterns and 5-year incidence of cardiovascular disease: a multivariate analysis of the ATTICA study.
\nAbstract: https:\/\/www.ncbi.nlm.nih.gov\/pubmed\/18722096<\/a>
\nFull text:https:\/\/www.nmcd-journal.com\/article\/S0939-4753(08)00132-4\/fulltext<\/a><\/p>\n

Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk.
\nhttps:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30594138<\/a><\/p>\n

"A study says machine learning algorithms applied to biopsy images can shorten the time for diagnosing and treating a gut disease that often causes permanent physical and cognitive damage in children."
\nhttps:\/\/www.sciencedaily.com\/releases\/2019\/06\/190617104125.htm<\/a><\/p>\n

Machine learning to analyze dietary intake
\nhttps:\/\/engineering.tufts.edu\/news\/2019\/05\/machine-learning-analyze-dietary-intake<\/a><\/p>\n

Deep Learning for Food Analysis
\nhttps:\/\/www.slideshare.net\/petiaivanova\/deep-learning-for-food-analysis<\/a><\/p>\n

Fast.ai: Using Deep Learning on Structured Data
\nhttps:\/\/mc.ai\/fast-ai-using-deep-learning-on-structured-data\/<\/a><\/p>\n

Machine Learning with Structured Data: Training the Model (Part 2)
\nhttps:\/\/cloud.google.com\/solutions\/machine-learning\/ml-on-structured-data-model-2<\/a><\/p>\n

Why is Deep Learning so bad for tabular data?
\nhttps:\/\/www.reddit.com\/r\/MachineLearning\/comments\/9826bt\/d_why_is_deep_learning_so_bad_for_tabular_data\/<\/a><\/p>\n

A Neural Network in PyTorch for Tabular Data with Categorical Embeddings
\nhttps:\/\/yashuseth.blog\/2018\/07\/22\/pytorch-neural-network-for-tabular-data-with-categorical-embeddings\/<\/a><\/p>\n

Sayed Ahmed<\/p>\n

Linkedin: https:\/\/ca.linkedin.com\/in\/sayedjustetc<\/a><\/p>\n

Blog: http:\/\/sitestree.com<\/a>, http:\/\/bangla.salearningschool.com<\/a> From: https:\/\/sitestree.com\/dietary-patterns-and-disease-machine-learning-deep-learning-statistical-methods\/
Categories:Root
Tags:
Post Data:2019-06-26 22:10:37<\/p>\n

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Dietary Patterns and Disease: Machine Learning, Deep Learning, Statistical Methods —- First the unusual one: Deep Learning and Tabular Data "Tabular data is the most commonly used type of data in industry, but deep learning on tabular data receives far less attention than deep learning for computer vision and natural language processing" https:\/\/www.fast.ai\/2018\/04\/29\/categorical-embeddings\/ 2nd the … <\/p>\n

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Reinforcement Learning is also one aspect of Machine Learning, Data Science, and AI Summary of Tabular Methods in Reinforcement Learning Comparison\u2026","rel":"","context":"In "AI ML DS RL DL NN NLP Data Mining Optimization"","block_context":{"text":"AI ML DS RL DL NN NLP Data Mining Optimization","link":"http:\/\/bangla.sitestree.com\/?cat=1910"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":14825,"url":"http:\/\/bangla.sitestree.com\/?p=14825","url_meta":{"origin":25049,"position":3},"title":"Machine Learning: Apply PCA on Datasets and Related","author":"Sayed","date":"June 22, 2019","format":false,"excerpt":"FactorAnalyzer https:\/\/github.com\/EducationalTestingService\/factor_analyzer --- sruti-jain\/Marketing-Analysis-for-Hotel-Chain-website https:\/\/github.com\/sruti-jain\/Marketing-Analysis-for-Hotel-Chain-website --- Understanding PCA (Principal Component Analysis) with Python https:\/\/towardsdatascience.com\/dive-into-pca-principal-component-analysis-with-python-43ded13ead21 The code for the most part will work though it used an earlier version of Python You will need module: StandardScaler Otherwise you might find the code below to be useful: [# ref: https:\/\/python-for-multivariate-analysis.readthedocs.io\/a_little_book_of_python_for_multivariate_analysis.html] import sklearn\u2026","rel":"","context":"In "AI ML DS RL DL NN NLP Data Mining Optimization"","block_context":{"text":"AI ML DS RL DL NN NLP Data Mining Optimization","link":"http:\/\/bangla.sitestree.com\/?cat=1910"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":76257,"url":"http:\/\/bangla.sitestree.com\/?p=76257","url_meta":{"origin":25049,"position":4},"title":"Can you answer these questions on Data Science Project Development","author":"Sayed","date":"August 24, 2024","format":false,"excerpt":"Can you answer these questions on Data Science Project Development Questions to answer 1. 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