PyTorch vs Stan

Overview

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

Stan

Stacks72

Followers27

Votes0

GitHub Stars2.7K

Forks379

PyTorch vs Stan: What are the differences?

Architecture: PyTorch is a deep learning library based on dynamic computation graphs, allowing for dynamic neural network architectures, while Stan is a probabilistic programming language that uses static computation graphs, suitable for Bayesian statistical models.
Gradient Computation: PyTorch computes gradients through automatic differentiation using the dynamic computational graph, making it well-suited for deep learning tasks, whereas Stan uses Hamiltonian Monte Carlo methods for gradient computation in Bayesian inference scenarios.
Flexibility: PyTorch offers more flexibility in defining and modifying complex neural network architectures due to its dynamic nature, allowing for easier experimentation and development compared to Stan, which focuses on probabilistic models and inference methods.
Ease of Use: PyTorch is more commonly used in the deep learning community and has a larger user base, resulting in better community support and a wider range of resources, while Stan is popular among statisticians and researchers focusing on Bayesian modeling and inference tasks.
Programming Paradigm: PyTorch follows an imperative programming paradigm, which makes it easier to build and debug models in a step-by-step manner, in contrast to Stan which follows a declarative programming paradigm that emphasizes specifying the structure of the model rather than the algorithmic process.
Deployment: PyTorch is well-suited for production deployment of machine learning models in various applications due to its integration with libraries like TorchServe and ONNX, while Stan is primarily used for research purposes and may not be as convenient for production deployment.

In Summary, PyTorch and Stan differ in architecture, gradient computation, flexibility, ease of use, programming paradigm, and deployment capabilities.

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Advice on PyTorch, Stan

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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Detailed Comparison

PyTorch	Stan
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.	A state-of-the-art platform for statistical modeling and high-performance statistical computation. Used for statistical modeling, data analysis, and prediction in the social, biological, and physical sciences, engineering, and business.
Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system	-
Statistics
GitHub Stars 94.7K	GitHub Stars 2.7K
GitHub Forks 25.8K	GitHub Forks 379
Stacks 1.6K	Stacks 72
Followers 1.5K	Followers 27
Votes 43	Votes 0
Pros & Cons
Pros 15 Easy to use 11 Developer Friendly 10 Easy to debug 7 Sometimes faster than TensorFlow Cons 3 Lots of code 1 It eats poop	No community feedback yet
Integrations
Python	Python Julia R Language Linux MATLAB GNU Bash

What are some alternatives to PyTorch, Stan?

JavaScript

JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.

Python

Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.

PHP

Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world.

Ruby

Ruby is a language of careful balance. Its creator, Yukihiro “Matz” Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming.

Java

Java is a programming language and computing platform first released by Sun Microsystems in 1995. There are lots of applications and websites that will not work unless you have Java installed, and more are created every day. Java is fast, secure, and reliable. From laptops to datacenters, game consoles to scientific supercomputers, cell phones to the Internet, Java is everywhere!

Golang

Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.

HTML5

HTML5 is a core technology markup language of the Internet used for structuring and presenting content for the World Wide Web. As of October 2014 this is the final and complete fifth revision of the HTML standard of the World Wide Web Consortium (W3C). The previous version, HTML 4, was standardised in 1997.

C#

C# (pronounced "See Sharp") is a simple, modern, object-oriented, and type-safe programming language. C# has its roots in the C family of languages and will be immediately familiar to C, C++, Java, and JavaScript programmers.

Scala

Scala is an acronym for “Scalable Language”. This means that Scala grows with you. You can play with it by typing one-line expressions and observing the results. But you can also rely on it for large mission critical systems, as many companies, including Twitter, LinkedIn, or Intel do. To some, Scala feels like a scripting language. Its syntax is concise and low ceremony; its types get out of the way because the compiler can infer them.

Elixir

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

Related Comparisons

PyTorch vs Stan: What are the differences?

Architecture: PyTorch is a deep learning library based on dynamic computation graphs, allowing for dynamic neural network architectures, while Stan is a probabilistic programming language that uses static computation graphs, suitable for Bayesian statistical models.
Gradient Computation: PyTorch computes gradients through automatic differentiation using the dynamic computational graph, making it well-suited for deep learning tasks, whereas Stan uses Hamiltonian Monte Carlo methods for gradient computation in Bayesian inference scenarios.
Flexibility: PyTorch offers more flexibility in defining and modifying complex neural network architectures due to its dynamic nature, allowing for easier experimentation and development compared to Stan, which focuses on probabilistic models and inference methods.
Ease of Use: PyTorch is more commonly used in the deep learning community and has a larger user base, resulting in better community support and a wider range of resources, while Stan is popular among statisticians and researchers focusing on Bayesian modeling and inference tasks.
Programming Paradigm: PyTorch follows an imperative programming paradigm, which makes it easier to build and debug models in a step-by-step manner, in contrast to Stan which follows a declarative programming paradigm that emphasizes specifying the structure of the model rather than the algorithmic process.
Deployment: PyTorch is well-suited for production deployment of machine learning models in various applications due to its integration with libraries like TorchServe and ONNX, while Stan is primarily used for research purposes and may not be as convenient for production deployment.

In Summary, PyTorch and Stan differ in architecture, gradient computation, flexibility, ease of use, programming paradigm, and deployment capabilities.

PyTorch vs Stan

Overview