Hacking the Coding/Machine Learning Interview: Tips Beyond “CTCI” :)
A study guide on preparing for software engineering and machine learning interviews 🙂
(Photo by Clément H on Unsplash)
Natural Language Processing (NLP) Fundamentals: Maximum Entropy (MaxEnt)
Ever wonder how computers might detect spam, discover sentiments in a product review, or guess a person’s favorite superhero? Or, perhaps you would like to know how a machine might differentiate dogs from fried chicken?
In my next post on Natural Language Processing (NLP) fundamentals, I discuss Maximum Entropy Classification, or MaxEnt for short.
Learn Machine Learning with Professor Andrew Ng on Coursera!
How almost every software engineer learns machine learning 😉
(Photo by Hitesh Choudhary on Unsplash)
Natural Language Processing (NLP) Fundamentals: Three Fundamental Tasks (of Hidden Markov Models [HMMs])
In this post, learn about three fundamental tasks of HMMs, including a soft introduction to generative modeling.
Natural Language Processing (NLP) Fundamentals: Hidden Markov Models (HMMs)
Ever wonder how computers “recognize” what you said, just from your voice?
Hidden Markov Models (HMMs) are a modern way for computers to perform speech recognition!
Natural Language Processing (NLP) Fundamentals: Finite State Transducers (FSTs)
Ever wonder how computers understand how to respond to simple commands, like “tell me a joke”?
Finite State Transducers (FSTs) can help computers do this, and more!
Natural Language Processing (NLP) Fundamentals
An introduction to a new blog series, Natural Language Processing Fundamentals
Boston Meetup: Anticipating the Future by Watching Unlabeled Video
My notes from the Boston Meetup, “Anticipating the Future by Watching Unlabeled Video”
Nade's Notes 