python


GetStream.io: Why We Switched from Python to Go – codeburst

@machinelearnbot

Switching to a new language is always a big step, especially when only one of your team members has prior experience with that language. Early this year, we switched Stream's primary programming language from Python to Go. This post will explain some of the reasons why we decided to leave Python behind and make the switch to Go. The performance is similar to that of Java or C . For our use case, Go is typically 30 times faster than Python.


which is the best book for python machine learning ? • r/Python

@machinelearnbot

I would recommend that you start with Introduction to Statistical Learning with R (usually shortened as ISLR). A lot of people have adapted the examples to Python if you google a bit and it's an excellent book that hides just enough complexity to not be overwhelming. Plus, once you have a good understanding of all of it, you can either graduate to the more extensive version (Elements of Statistical Learning, usually shortened as ESL) for a more rigorous treatment of the same thing, or choose to go for something different like Bishop's Pattern Recognition and Machine Learning. ISLR is free as a pdf and has a corresponding MOOC. ESL doesn't, but is also free on the author's website.


TensorFlow for Real-World Applications - DZone AI

#artificialintelligence

This article is featured in the new DZone Guide to Artificial Intelligence. Get your free copy for more insightful articles, industry statistics, and more! I have spoken to thought leaders at a number of large corporations that span across multiple industries such as medical, utilities, communications, transportation, retail, and entertainment. They were all thinking about what they can and should do with deep learning and artificial intelligence. They are all driven by what they've seen in well-publicized projects from well-regarded software leaders like Facebook, Alphabet, Amazon, IBM, Apple, and Microsoft.


Profiting from Python & Machine Learning in the Financial Markets

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I finally beat the S&P 500 by 10%. This might not sound like much but when we're dealing with large amounts of capital and with good liquidity, the profits are pretty sweet for a hedge fund. More aggressive approaches have resulted in much higher returns. It all started after I read a paper by Gur Huberman titled "Contagious Speculation and a Cure for Cancer: A Non-Event that Made Stock Prices Soar," (with Tomer Regev, Journal of Finance, February 2001, Vol. "A Sunday New York Times article on a potential development of new cancer-curing drugs caused EntreMed's stock price to rise from 12.063 at the Friday close, to open at 85 and close near 52 on Monday.


Predicting Political Bias with Python – Linalgo – Medium

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Recent scandals around fake news have spurred an interest in programmatically gauging the journalistic quality of an article. Companies like Factmata and Full Fact have received funding from Google, and Facebook has launched its "Journalism Project" earlier this year to fight the spread of fake stories in its feed. Discriminating between facts and fake information is a daunting task but often times, looking at the publisher is a good proxy to gauge the journalistic quality of an article. And while there is no objective metric to evaluate the quality of a newspaper, its overall quality and political bias is generally agreed upon (one can for example refer to https://mediabiasfactcheck.com/). In this article, we present a few techniques to automatically assess the journalistic quality of a newspaper.


Azure ML workbench- Installation-Part 1

#artificialintelligence

In Microsoft ignite 2017, Azure ML team announce new on-premises tools for doing machine learning. So, I have to go through the "Azure Portal" to create an "Experimentation Account". Moreover, you able to create an account for machine learning model. in below the picture, I created "Machine Learning Model Managment" Now I am able to open the "Azure ML workbench" So as you see in above there are no Projects, I have to create one. As it is my first time to work with this tool, I am going to try one of the sample projects there. So, just put a name for the project name, project directory, and then simply create it.


At GitHub, JavaScript rules in usage, TensorFlow leads in forks

@machinelearnbot

JavaScript is the most-popular language on GitHub, based on pull requests from the popular code-sharing site. Since September 2016, there have been 2.3 million pull requests for JavaScript, GitHub reports. Following web development staple JavaScript was Python, with 1 million requests, and Java, with 986,000 requests. Python displaced Java as the second-most-popular language on GItHub. Also improving its lot greatly in 2017 was TypeScript, Microsoft's typed superset of JavaScript, which had 207,000 pull requests, almost four times as many requests as it had the year before.


Data Revenue: Machine Learning Developer

@machinelearnbot

We are Data Revenue (datarevenue.com), We are a 3 machine learning engineers and few freelancers, working out of Berlin and Krakow. Over the last 2 1/2 years we worked on some great projects in Web, Finance, Energy and Medical. Doing purchase/demand prediction, churn prediction, credit scoring and fraud detection. We actively choose clients with interesting problems and datasets.


Intro to Pandas: -1 : An absolute beginners guide to Machine Learning and Data science.

#artificialintelligence

Pandas is hands down one of the best libraries of python. It supports reading and writing excel spreadsheets, CVS's and a whole lot of manipulation. It is more like a mandatory library you need to know if you're dealing with datasets from excel files and CSV files. This is part one of Pandas tutorial. I'm not going to cover everything possible with pandas, however, I want to give you a taste of what it is and how you can get started with it.


Writing Julia functions in R with examples

@machinelearnbot

The Julia programming language is growing fast and its efficiency and speed is now well-known. Even-though I think R is the best language for Data Science, sometimes we just need more. Modelling is an important part of Data Science and sometimes you may need to implement your own algorithms or adapt existing models to your problems. If performance is not essential and the complexity of your problem is small, R alone is enough. However, if you need to run the same model several times on large datasets and available implementations are not suit to your problem, you will need to go beyond R. Fortunately, you can go beyond R in R, which is great because you can do your analysis in R and call complex models from elsewhere.