In this article, I am going to show you how to choose the number of principal components when using principal component analysis for dimensionality reduction.
In the first section, I am going to give you a short answer for those of you who are in a hurry and want to get something working. Later, I am going to provide a more extended explanation for those of you who are interested in understanding PCA.
Perplexity is a useful metric to evaluate models in Natural Language Processing (NLP). This article will cover the two ways in which it is normally defined and the intuitions behind them. A language…
At the very beginning of the tutorial, I’ll explain the dimensionality of a dataset, what dimensionality reduction means, the main approaches to dimensionality reduction, the reasons for dimensionality reduction and what PCA means. Then, I will go deeper into the topic of PCA by implementing the PCA algorithm with the Scikit-learn machine learning library. This will help you to easily apply PCA to a real-world dataset and get results very fast.
As data scientists, we spend a lot of our time doing exploratory data analysis (EDA), cleaning data and making sure the data we use to generate insights is of good quality. Have you ever found…
When the downstream applications only care about the direction of the word vectors (e.g. they only pay attention to the cosine similarity of two words), then normalize, and forget about length.
However, if the downstream applications are able to (or need to) consider more sensible aspects, such as word significance, or consistency in word usage (see below), then normalization might not be such a good idea.
Comparing machine learning methods and selecting a final model is a common operation in applied machine learning.
Models are commonly evaluated using resampling methods like k-fold cross-validation from which mean skill scores are calculated and compared directly. Although simple, this approach can be misleading as it is hard to know whether the difference between mean skill scores is real or the result of a statistical fluke.
There are currently few datasets appropriate for training and evaluating models for non-goal-oriented dialogue systems (chatbots); and equally problematic, there is currently no standard procedure for evaluating such models beyond the classic Turing test.
The aim of our competition is therefore to establish a concrete scenario for testing chatbots that aim to engage humans, and become a standard evaluation tool in order to make such systems directly comparable.
I have been working with LangChain applications for quite a while now and as you might know there is always something new to learn in the GenAI universe. So a couple of weeks ago I was going through…
B. Pang, и L. Lee. Proceedings of the Association for Computational Linguistics (ACL), стр. 271--278. Association for Computational Linguistics, (2004)
S. Basu, A. Banerjee, и R. Mooney. Proceedings of the 2004 SIAM International Conference on Data Mining, стр. 333--344. Lake Buena Vista, FL, Society for Industrial and Applied Mathematics, (апреля 2004)
H. Zhuang, T. Hanratty, и J. Har. Proceedings of the 2019 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, (мая 2019)
R. Snow, B. O'Connor, D. Jurafsky, и A. Ng. EMNLP '08: Proceedings of the Conference on Empirical Methods in Natural Language Processing, стр. 254--263. Morristown, NJ, USA, Association for Computational Linguistics, (2008)
R. Snow, B. O'Connor, D. Jurafsky, и A. Ng. EMNLP '08: Proceedings of the Conference on Empirical Methods in Natural Language Processing, стр. 254--263. Morristown, NJ, USA, Association for Computational Linguistics, (2008)
H. Danner, G. Hagerer, F. Kasischke, и G. Groh. Conference Proceedings of "3rd International Conference on Advanced Research Methods and Analytics", стр. 211-219. Valencia, Spain, Editorial Universitat Politècnica de València, (июля 2020)
N. Hossain, J. Krumm, и M. Gamon. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), стр. 133--142. (2019)
Working group for social relation and opinion mining
Research Group Social Computing
Department of Informatics
Technical University of Munich (TUM)
http://www.social.in.tum.de/ghagerer