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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Gensim Doc2Vec on Spark - a quest to get the right Vector

Ever since I joined the R & D group we have been doing a lot of cool things, like trying IBM Watson (see previous blog entry). Now we are doing a lot of Natural language processing.  We wanted to compare the similarity of two documents. There is this excellent project Gensim ( doc2vec ) that easily allow to you to translate large blocks of text to a fixed length feature vector to make comparisons. Here is the link to the  original paper  from some people from Google that explains the approach. In essence, they wanted to find a representation that will overcome the weaknesses of the bag of words model. The doc2vec approach proves to be a reliable approach to comparing similarities of documents because it takes into consideration the semantics and the order of the word in context. So with that, we wanted to use it for a corpus of 26 million documents. Calculating the doc2vec for 26 million documents is not a small task, so we need to process it in Spark. The problem ...
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OAuth 1.0a Request Signing and Verification - HMAC-SHA1 - HMAC-SHA256

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This is a security project that I did this Fall semester. I was interested in why the OAuth1.0a specification did not require you to sign a request using RSA-SHA1 or any RSA algorithm. The first issue that I found is where would you store your private keys in a javascript client? - as a javascript encrypted file ? from the browser ?  - that will be a security hole if you allow javascript to get private keys. These are some of the questions that got me asking and which I cannot find answers to for now. OAuth 1.0a Signature Signing and Verification using JavaScript Implementation Background The OAuth specification tries to define an API authentication method that allows consumer websites or applications to access protected API Resources from a Service provider. The advantage that OAuth provides is that the user of the website or application is not required to disclose their credentials to their service provider. Another advantage of using OAuth to authenticate is that it does...
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