September 23, 2020
Importance of C++ in Data Science and Big Data Introduction and Motivation – Why C++ C++ is ideal for dynamic load balancing, adaptive caching, and developing large big data frameworks, and libraries. Google’s MapReduce, MongoDB, most of the deep learning libraries listed below have been implemented using C++. Scylla known for its ultra-low latency and extremely high throughput is coded using C++ acts as a replacement to Apache Cassandra and Amazon DynamoDB. With some of […]
Introduction Sentiment analysis has been predominantly used in data science for analysis of customer feedbacks on products and reviews. They are used to understand user ratings on different kinds of products, hospitality services like travel, hotel bookings. It has also become popular to analyse user tweets — positive, negative or neutral by crawling twitter through APIs. In this article, we talk about sentiment analysis of the upcoming Lokshobha Elections for Congress and BJP by crawling […]
Neural Architecture Search with NASBench from Google Research— Can we design network architectures automatically, instead of relying on expert experience and knowledge? Motivation Recent advances in neural architecture search (NAS) has vastly increased demand of high-end servers having tremendous computational resources. As the cost of maintaining high-end servers is high, it becomes inaccessible to students and researchers. As a result, it creates a system limitation when students and researchers are not able to reproduce experiments […]
April 27, 2020
Logistic Regression with Google’s AdaNet- Auto Learning Ensemble Framework Motivation AdaNet provides a framework that could automatically produce a high-quality model given an arbitrary set of features and a model search space. In addition, it builds ensembles from productionized TensorFlow models to: Reduce churn, reuse domain knowledge, and conform with business and explainability requirements. The framework is capable of handling datasets containing thousands to billions of examples, in a distributed environment. Several open-source AutoML frameworks, […]
Profiling Statistics of Python Libraries and Insights of New/Emerging Factorization Techniques Introduction In my previous blog, I narrated about different matrix factorization techniques, citing pros and cons of each of the libraries. In this blog, I will give some code samples of some of the previously discussed libraries. We will also see the profiling statistics of some of the python libraries (for SVD/Recommendation Engine) so as to understand the time taken to execute the training engine, […]
