Seeking Wisdom

AI/ML Engineering

Notes on building machine learning systems — the models, and the less glamorous work of getting them into production. Much of it starts with NLP and deep learning; the newer posts are about serving, MLOps, Kubernetes and agents. Everything here is something I built, broke, or finally understood well enough to write down.

2019

2017

  • Sentiment Analysis for User Reviews

    We create a system to assign sentiment scores to reviews (0.0 to 1.0 where a zero score is a full negative sentiment)

  • Deep Learning based Email Spam Filter

    We create Deep Learning based Email Spam Filter on Enron and Spam assassin dataset and compare results with Xgboost, SVM and Random forest

  • Semantic Role Labeling

    Semantic Role Labeling consists of the detection of the semantic arguments associated with the predicate or verb of a sentence and their classification into their specific roles.

  • Statistical Inference

    Statistics are the foundations of Data Analysis. Statistical Inference is a process of deducing properties of an underlying distribution by analysis of data.

  • Research Summary Log

    A summary log of read research papers in the field of AI

2016

  • Meanings are Vectors

    A recent big idea in natural language processing is that "meanings are vectors" . Word embeddings are one of the most exciting area of research in deep learning.

  • English Grammar Analysis Demo

    Grammar Analysis of English Sentences using Syntactic Rules based on English Grammar. The System is designed to be generic using only standard english grammar rules.

  • NLP Modules

    Various Modules from NLP Pipeline

  • Simplified Switchboard Corpus

    We will create a simplified version of Switchboard Speech act corpus and perform exploratory

2015

2014