Software Alternatives & Reviews

MC Stan VS NLTK

Compare MC Stan VS NLTK and see what are their differences

MC Stan logo MC Stan

Stan is a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences.

NLTK logo NLTK

NLTK is a platform for building Python programs to work with human language data.
  • MC Stan Landing page
    Landing page //
    2023-08-18
  • NLTK Landing page
    Landing page //
    2023-01-25

MC Stan videos

MC STΔN NUMBERKARI REACTION | MC STAN NUMBERKARI REACTION | MC STAN NEW SONG | TADIPAAR 2K20 | AFAIK

More videos:

  • Review - What is MC STAN ? Is he really worth all the hype? TADIPAAR ALBUM REVIEW | Desi Hip-Hop
  • Review - MC STΔN AMIN REACTION | AMIN REACTION | MC STAN AMIN REACTION | MC STAN REACTION | TADIPAAR | AFAIK

NLTK videos

29 Python NLTK Text Classification Sentiment Analysis movie reviews

More videos:

  • Review - Tutorial 24: Sentiment Analysis of Amazon Reviews using NLTK VADER MODULE PYTHON with [SOURCE CODE]

Category Popularity

0-100% (relative to MC Stan and NLTK)
Data Science And Machine Learning
Spreadsheets
0 0%
100% 100
Data Science
100 100%
0% 0
Natural Language Processing

User comments

Share your experience with using MC Stan and NLTK. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, MC Stan should be more popular than NLTK. It has been mentiond 24 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

MC Stan mentions (24)

  • [Q] Is there a method for adding random effects to an interval censored time to event model?
    My approach to problems like this is to write down the proposed model mathematically first, in extreme detail. I find hierarchical form to be the easiest way to break it down piece by piece. Once I have the maths then I turn it into a Stan model. Last step is to use the Stan output to answer the research questions. Source: 11 months ago
  • Demand Planning
    For instance my first choice in these cases is always a Bayesian inference tool like Stan. In my experience as someone who’s more of a programmer than mathematician/statistician, Bayesian tools like this make it much easier to not accidentally fool yourself with assumptions, and they can be pretty good at catching statistical mistakes. Source: 12 months ago
  • What do actual ML engineers think of ChatGPT?
    I tend to be most impressed by tools and libraries. The stuff that has most impressed me in my time in ML is stuff like pytorch and Stan, tools that allow expression of a wide variety of statistical (and ML, DL models, if you believe there's a distinction) models and inference from those models. These are the things that have had the largest effect in my own work, not in the sense of just using these tools, but... Source: 12 months ago
  • How to get started learning modern AI?
    Oh its certainly used in practice. You should look into frameworks like Stan[1] and pyro[2]. I think bayesian models are seen as more explainable so they will be used in industries that value that sort of thing [1] https://mc-stan.org/. - Source: Hacker News / about 1 year ago
  • Should I start learning R, SAS, or Python during my gap year?
    At this point the only people using such things are the programmers. Think e.g. STAN. https://mc-stan.org/ the rest of us: R, SAS, Excel. Source: about 1 year ago
View more

NLTK mentions (3)

  • Just created an app to help me practice my Polish grammar. The passages are from classical literature available in the public domain. If you would like to try it, the link is in the comments.
    To give you some further inspiration, you might want to check out the NLTK (Natural Language Toolkit - https://www.nltk.org/ ). It is a huge collection of tools for language data processing in general. Source: 12 months ago
  • Which not so well known Python packages do you like to use on a regular basis and why?
    I work mostly in the NLP space, so other libraries I like are spaCy, nltk, and pynlp lib. Source: over 1 year ago
  • How to make/program an AI? Is it even possible?
    Learn some Python and play around with existing AI libraries. Go through things like nltk.org and some freecodecamp tutorials to get some hands-on knowledge. Follow this sub and watch the kinds of projects people are creating. Source: over 2 years ago

What are some alternatives?

When comparing MC Stan and NLTK, you can also consider the following products

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

spaCy - spaCy is a library for advanced natural language processing in Python and Cython.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Amazon Comprehend - Discover insights and relationships in text

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Google Cloud Natural Language API - Natural language API using Google machine learning