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Weekly Selection – Feb 8, 2019

What I Learned from Writing a Data Science Article Every Week for a Year

By Will Koehrsen – 13 min read

There ought to be a law limiting people to one use of the term "life-changing" to describe a life event. Had a life-changing cup of coffee this morning? Well, hope it was good because that’s the one use you get!


Making deep neural networks paint to understand how they work

By Paras Chopra – 10 min read

It’s a mystery that deep learning works so well. Even though there are several hints about why deep neural networks are so effective, the truth is that nobody is entirely sure and theoretical understanding of deep learning is very much an active area of research.


PyViz: Simplifying the Data Visualisation process in Python.

By Parul Pandey – 9 min read

If you are working with data, then Data Visualisation is a vital part of your daily routine. And if you use Python for your analysis, you ought to be overwhelmed by the sheer amount of choices present in form of Data Visualisation libraries.


How It Feels to Learn Data Science in 2019

By Thomas Nield – 16 min read

I have decided I can no longer ignore data science, artificial intelligence, and machine learning. I have worked as an analyst and consultant for years shuffling numbers around in Excel workbooks, doing pivot tables, and making charts.


The keys of Deep Learning in 100 lines of code

By Javier Ideami – 13 min read

These are exciting times for those passionate about the mysteries and possibilities of deep learning. Many of the heroes in the field share their expertise through videos and articles.


Unsupervised learning for anomaly detection in stock options pricing

By Boris B – 8 min read

Options valuation is a very difficult task. To begin with, it entails using a lot of data points (some are listed below) and some of them are quite subjective (such as the implied volatility – see below) and difficult to calculate precisely.


Learn Enough Python to be Useful: argparse

By Jeff Hale – 6 min read

If you plan to be a software developer with Python, you’ll want to be able to use argparse for your scripting needs. If you’re a data scientist, you’ll likely find yourself needing to port your code from a Jupyter Notebook to a reproducible script.


Introducing the AI Project Canvas

By Jan Zawadzki – 9 min read

Creating an AI Project always involves answering the same questions: What is the value you’re adding? What data do you need? Who are the customers? What costs and revenue are expected?


Predicting a Startup Valuation with Data Science

By Sebastian Quintero – 14 min read

It’s often difficult to comprehend the significance of numbers thrown around in the startup economy. If a company raises a $550M Series F at a valuation of $4 billion – how big is that really? How does that compare to other Series F rounds?


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