Deep Learning, NLP, NMT, AI, ML
Python tutorials
You may need to add Python to PATH if you’ve installed Python, but typing python on the command line doesn’t seem to work. You may be getting a message saying that the term python isn’t recognized, or you may end up with the wrong version of Python running. A common fix for these problems is adding Python to the PATH environment variable. In this tutorial, you’ll learn how to add Python to PATH. You’ll also learn about what PATH is […]
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I recently came across a situation where DuckDB fit the need on a recent project. The ask was to demonstrate the construction of a data pipeline and highlight the analytical possibilities of a spatial tracking dataset. DuckDB is an impressive in-process single-file database that proved to be blazingly fast, but also an absolute delight to code against.
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In this series of posts I am trying to break down one of the complicated and intriguing topics in python. Please visit my last post to understand why multi threading is broken in python(Take this comment with a pinch of salt). https://www.reddit.com/r/Python/comments/xdyahc/multithreading_a_concept_which_is_always/
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In this article, I want to show the approach that we are going to use in our project of podcast summarization. To summarize text correctly we first need to split a text into meaningful parts—paragraphs. · General approach·
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Apply Topic Modeling algorithms using Gensim — before moving on to more advanced textual analysis techniques
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Hi Folks, If you are a data scientist working with machine learning models you may know that our model understands numerical values. While working with Natural language processing to make our models understand our words we need to embed our words into vectors.
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Given two collections of entities, such as product listings, the entity matching (EM) problem aims to identify all pairs that refer to the same object in the real world, such as products, publications, businesses, etc. Recently, deep learning (DL) techniques have been widely applied to the EM problem and have achieved promising results. Unfortunately, the performance gain brought by DL techniques comes at the cost of reducing transparency and interpretability. The reason is that DL-based approaches are more like black-box […]
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