Stokastik

Machine Learning, AI and Programming

Category: MACHINE LEARNING

Designing large scale similarity models using deep learning

Finding similar texts or images is a very common problem in machine learning used extensively for search and recommendation. Although the problem is very common and has high business value to some organisations, but still this has remained one of the most challenging problems when the database size is very large such as >50GB and we do not want to lose on precision and recall much by retrieving only 'approximately' […]

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Detecting placeholder images in e-Commerce product listings

Many times we trust sellers to upload correct product images for online products on our e-commerce platform, but due to checks in place that a product shall always accompany an image, 3rd party sellers upload stock or placeholder image in case there is no image for the product available. Problem is that although we do not want to show products without images on our website, but due to zero validation […]

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Building a classification pipeline with C++11, Cython and Scikit-Learn

We have earlier seen how using Cython increases the performance of Python code 50-60x, mostly due to static typing as compared to dynamic typing in pure Python. But we have also seen how one can wrap pure C++ classes and functions with Cython and export them as Python packages with improved speed. The codes we have dealt with so far using Cython were mostly generic modules like generating primes using […]

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Speeding up with Cython

While I was working with the R programming language, I always somehow found it to be slow coming from a JAVA/C++ background. Then I discovered the "RCpp" package which allowed me to write C++ codes and call them from R. It greatly improved my program speed by an order of 50-100x. Most of the coding that I did was in C++ while the interface remained in R. Since I moved […]

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BiLSTM-CRF Sequence Tagging for E-Commerce Attribute Extraction

In the last post we had used Conditional Random Fields (CRF) to extract attributes from e-commerce product titles and description. CRFs are linear models just like Logistic Regression. The drawback with linear models is that they do not take feature-feature interaction or higher order feature terms into account while building model. Linear models can under-fit on the data while too much non-linearity can lead to over-fitting.┬áNon-linear models such as Neural […]

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Attribute Extraction from E-Commerce Product Description

In this post we are going to look into how one can use product title and description on e-commerce websites to extract different attributes of the product. This is a very fundamental problem in e-commerce which has widespread implications for Product Search (search filters), Product Matching (matching same items from different sellers), Product Grouping (grouping items by variants such as size and color), Product Graph (relationship between products based on […]

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Factorization Machines for Movie Recommendations

In the last series of posts we have looked at how to recommend movies to users based on the historical ratings. The two most promising approaches were Collaborative Filtering and Matrix Factorization. Both these approaches learns the user-movie preferences only from the ratings matrix. Recall that in the first post of the series, we had started with an approach known as the Content Based Recommendation, where we created a regression […]

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Designing Movie Recommendation Engines - Part III

In the last two parts of this series, we have been looking at how to design and implement a movie recommendations engine using the MovieLens' 20 million ratings dataset. We have looked at some of the most common and standard techniques out there namely Content based recommendations, Collaborative Filtering and Latent Factor based Matrix Factorization strategy. Clearly CF and MF approaches emerged as the winners due to their accuracy and […]

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Designing Movie Recommendation Engines - Part II

In the last post, we had started to design a movie recommendation engine using the 20 million ratings dataset available from MovieLens. We started with a Content Based Recommendation approach, where we built a classification/regression model for each user based on the tags and genres assigned to each movie he has rated. The assumption behind this approach is that, the rating that an user has given to a movie depends […]

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Designing Movie Recommendation Engines - Part I

In this post, we would be looking to design a movie recommendation engine with the MovieLens dataset. We will not be designing the architecture of such a system, but will be looking at different methods by which one can recommend movies to users that minimizes the root mean squared error of the predicted ratings from the actual ratings on a hold out validation dataset.

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