Tech Doodling

How Computers Understand Human Language? Photo by Alex Knight on Unsplash Natural languages are the languages that we speak and understand, containing large diverse vocabulary. Various words have several different meanings, speakers with different accents and all sorts of interesting word play. But for the most part human can roll right through these challenges. The skillful use of language is a major part what makes us human and for this reason the desire for computers that understand or speak human language has been around since they were first conceived. This led to the creation of natural language processing or NLP. Natural Language Processing is a disciplinary field combining computer science and linguistics. There is an infinite number of ways to arrange words in a sentence. We can't give computers a dictionary of all possible sentences to help them understand what humans are blabbing on about. So, an early and fundamental NLP problem was deconstructing sentences...

unsplash-logo Franck V. For all these years, we all have been misguided by the term machine learning. We have been told that machines learning makes a machine capable of how to think, how to act like a human. Machine learning is the most misused term. It does not really mean what it sounds like. It is a lie, a truthy lie. What is meant by a truthy lie? Each year Merriam-Webster releases a top 10 list of most searched words. In 2003, the top word in the list was democracy. In 2004, the word blog made it to the top. The winning word for the year 2006 was trustiness, "Truth coming from the gut, not books; preferring to believe what you wish to believe, rather than what is known to be true" . A word which could be a lie is used so often that it eventually feels like truth. "Bet on the jockey, not the horse" is a truthy lie. Similarly, "machine learning" has been used over time for any kind of activity to train a machine or a computer so it could t...

Supervised Learning vs Unsupervised Learning vs Reinforcement Learning Machine learning models are useful when there is huge amount of data available, there are patterns in data and there is no algorithm other than machine learning to process that data. If any of these three conditions are not satisfied, machine learning models are most likely to under-perform. Machine learning algorithms find patterns in data and try to learn from it as much as it can. Based on the type of data available and the approach used for learning, machine learning algorithms are classified in three broad categories. Supervised learning Unsupervised learning Reinforcement learning An abstract definition of above terms would be that in supervised learning, labeled data is fed to ML algorithms while in unsupervised learning, unlabeled data is provided. There is a another learning approach which lies between supervised and unsupervised learning, semi-supervised learning. Semi supervised learning al...