1677924194
MXFusion: Modular Probabilistic Programming on MXNet
MXFusion
MXFusion is a modular deep probabilistic programming library.
With MXFusion Modules you can use state-of-the-art inference techniques for specialized probabilistic models without needing to implement those techniques yourself. MXFusion helps you rapidly build and test new methods at scale, by focusing on the modularity of probabilistic models and their integration with modern deep learning techniques.
MXFusion uses MXNet as its computational platform to bring the power of distributed, heterogenous computation to probabilistic modeling.
Installation
Dependencies / Prerequisites
MXFusion's primary dependencies are MXNet >= 1.3 and Networkx >= 2.1. See requirements.
Supported Architectures / Versions
MXFusion is tested on Python 3.4+ on MacOS and Linux.
Installation of MXNet
There are multiple PyPi packages of MXNet. A straight-forward installation with only CPU support can be done by:
pip install mxnet
For an installation with GPU or MKL, detailed instructions can be found on MXNet site.
pip
If you just want to use MXFusion and not modify the source, you can install through pip:
pip install mxfusion
From source
To install MXFusion from source, after cloning the repository run the following from the top-level directory:
pip install .
Where to go from here?
Community
We welcome your contributions and questions and are working to build a responsive community around MXFusion. Feel free to file an Github issue if you find a bug or want to request a new feature.
Contributing
Have a look at our contributing guide, thanks for the interest!
Points of contact for MXFusion are:
- Eric Meissner (@meissnereric)
- Zhenwen Dai (@zhenwendai)
Download Details:
Author: amzn
Source Code: https://github.com/amzn/MXFusion
License: Apache-2.0 license
#machinelearning #python #mxnet #probabilistic #programming
What is GEEK
Buddha Community
1677924194
MXFusion: Modular Probabilistic Programming on MXNet
MXFusion
MXFusion is a modular deep probabilistic programming library.
With MXFusion Modules you can use state-of-the-art inference techniques for specialized probabilistic models without needing to implement those techniques yourself. MXFusion helps you rapidly build and test new methods at scale, by focusing on the modularity of probabilistic models and their integration with modern deep learning techniques.
MXFusion uses MXNet as its computational platform to bring the power of distributed, heterogenous computation to probabilistic modeling.
Installation
Dependencies / Prerequisites
MXFusion's primary dependencies are MXNet >= 1.3 and Networkx >= 2.1. See requirements.
Supported Architectures / Versions
MXFusion is tested on Python 3.4+ on MacOS and Linux.
Installation of MXNet
There are multiple PyPi packages of MXNet. A straight-forward installation with only CPU support can be done by:
pip install mxnet
For an installation with GPU or MKL, detailed instructions can be found on MXNet site.
pip
If you just want to use MXFusion and not modify the source, you can install through pip:
pip install mxfusion
From source
To install MXFusion from source, after cloning the repository run the following from the top-level directory:
pip install .
Where to go from here?
Community
We welcome your contributions and questions and are working to build a responsive community around MXFusion. Feel free to file an Github issue if you find a bug or want to request a new feature.
Contributing
Have a look at our contributing guide, thanks for the interest!
Points of contact for MXFusion are:
- Eric Meissner (@meissnereric)
- Zhenwen Dai (@zhenwendai)
Download Details:
Author: amzn
Source Code: https://github.com/amzn/MXFusion
License: Apache-2.0 license
1601204400
Probabilistic programming
What is Probabilistic programming?
It helps to create “statistical models” of real-world processes.
“Probabilistic programming allows for incorporating domain knowledge in the models and makes the machine learning system more interpretable”
Working of Infer.Net
Infer.NET process by compiles a model definition into the source code required to output a set of inference queries on the model. The diagram below summarises the inference process.
Source Microsoft
The steps are:
- The user creates a model definition and specifies a collection of inference queries related to that model.The user shares the model definition and inference queries to the model compiler, which creates the source code — this writes the source code into a file.Compile source code to create an algorithm.With a set of observed values, the inference engine executes the compiled algorithm.
#dotnet-core #machine-learning #probabilistic #csharp #probabilistic-programming
1597824000
Coding 101: Programming Language Building Blocks
This article will introduce the concepts and topics common to all programming languages, that beginners and experts must know!
Do you want to learn a programming language for the first time?
Do you want to improve as a Programmer?
Well, then you’re in the right place to start. Learn any programming language without difficulty by learning the concepts and topics common to all programming languages.
Let me start by answering the following questions:
- Why learn Programming?
- What is Programming?
- How to Learn a Programming Language?
Why learn Programming❔
Programming develops creative thinking
Programmers solve a problem by breaking it down into workable pieces to understand it better. When you start learning to program, you develop the habit of working your way out in a very structured format. You analyze the problem and start thinking logically and this gives rise to more creative solutions you’ve ever given.
Whether you want to uncover the secrets of the universe, or you just want to pursue a career in the 21st century, basic computer programming is an essential skill to learn.
_– _Stephen Hawking
Everybody in this country should learn how to program a computer… because it teaches you how to think.
_- _Steve Jobs
Programming Provides Life-Changing Experiences
Programming always provides you with a new challenge to take risks every time and that teaches you to take risks in your personal life too. The world is filled up with websites, apps, software and when you build these yourself you’ll feel more confident. When a programmer solves a problem that no one has ever solved before it becomes a life-changing experience for them.
What is Programming🤔?
A program is a set of instructions to perform a task on a computer.
Programming is the process of designing and building an executable computer program to accomplish a specific task.
Well, according to me programming is like raising a baby. We provide knowledge (data) to help understand a baby what’s happening around. We teach a baby to be disciplined (and much more) by making rules.
Similarly, a computer is like a baby. We set rules and provide data to the computer through executable programs with the help of a Programming Language.
(Photo by Clément H on Unsplash)
That’s it👍. If you can understand this basic concept of programming, you’re good to go. Pick up a programming language and start learning. Read the following section to get an idea of where to start.
My recommendation is to choose Python Programming Language as a start, because it’s beginner-friendly.
#programming #programming-tips #programming-language #programming-top-story #computer-science #data-structures-and-algorithms #tips-for-programmers #coding
1596725220
Intro to probabilistic programming
What is Probabilistic Programming?
The idea behind Probabilistic programming to bring the inference algorithms and theory from statistics combined with formal semantics, compilers, and other tools from programming languages to build efficient inference evaluators for models and applications from Machine Learning. In other words, probabilistic programming is a tool for statistical modeling. The idea is to borrow lessons from the world of programming languages and apply them to the problems of designing and using statistical models.
Probabilistic programming is about doing statistics using the tools of computer science.
#data science #probabilistic programming #programming languages #statistical modeling
1598330580
Programming In Acceleration: Levelling Up Programming Skills
Some require and some are not. But acceleration programs might require you to build one. I’ll tell you how I made a computer program for the competition.
1-Researched the source codes and pasted it on my VS code
Written on the internet “blockchain-based ticket codes” and found the Ethereum source codes in Github. Then, I’ve just copied and pasted on my VS code by naming with .sol extension. Then, I’ve got my hands on the code itself and started to correct the mistakes that the editor has shown so far. Managed to reduce 189 errors to 58 within two hours. The rest was handled by my teammate when I sent the code I’ve edited. He just fixed the codes in three more hours and my mistake was not to increase the gas price. We increased the gas price on the remix and everything worked. And he just tested the software on scalability and security. It was the perfect garment for us that everything worked except the indentation errors.
What should’ve been done by us
Found all the codes including testing, copied them, and pasted them to our text editor for further analysis. Still, we had the prototype and we could write all the test codes, migrations, etc. if needed. Even more, we should’ve researched the codes to our project before using one of the examples.
#acceleration-program #program-analysis #programming #startup #acceleration #data analysis