2016 - Movies Data Analysis - Linear Regression Modelling

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Attention not needed!

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Attention, Attention For as long as we have been using Transformer models, there has been a question about how to put it in production because it needs a lot of computing power. Google took about a year from when BERT (Bidirectional Encoder Representations from Transformers) was introduced to being incorporated into their search algorithm. It is not just the advancement in understanding searches using BERT; they also needed new hardware. “But it’s not just advancements in software that can make this possible: we needed new hardware too. Some of the models we can build with BERT are so complex that they push the limits of what we can do using traditional hardware, so for the first time, we’re using the latest  Cloud TPUs  to serve search results and get you more relevant information quickly. “( https://blog.google/products/search/search-language-understanding-bert/ ) One of the complexities in BERT is the ‘self-attention’ modules, these are neural network layers that compu...
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Speech to Text - HTML 5

Technology Can Help Recently there was a news in Good Morning America were a deaf cashier is taking orders and customers patiently writing out there orders. Here is a link   Deaf Cashier Well with a computer in every pocket there is technology to help. Here is a quick demo that i have created to use HTML 5 technology to create a speech to text web page. Here is a link to the working page http://petabyte.github.io/textToSpeech.html https://www.youtube.com/watch?v=27SZISZAPEA
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Predicting Helpful Posts

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Original paper:  https://www.aclweb.org/anthology/N19-1318 Here is a quick summary: The research purpose is to identify helpful posts from discussion threads in forums, especially long-running discussions. The approach is to model the relevance of each post concerning the original post and the novelty (not presented in the earlier posts of the discussion thread)of a  post based on a windowed context. To model, the 'relevance' the original post and the target post are encoded using an RNN (GRU). The encoded sequences are then element-wise multiplied. As for modeling of the 'novelty,' the target post and the past K posts (where K is the number of past posts taken into context. A 'K' between 11 to 7 worked best for the Reddit dataset used in the experiment - performance stops improving after a certain number of posts taken into context) are also encoded using the same RNN text encoder. Once the 'K' posts are text encoded it is then fed thru another R...
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