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Weekly Selection – Mar 30, 2018

Here's why so many data scientists are leaving their jobs

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About Us | Feedback

by Jonny Brooks-Bartlett – 8 min read

Yes, I am a data scientist and yes, you did read the title correctly, but someone had to say it. We read so many stories about data science being the sexiest job of the 21st century and the attractive sums of money that you can make as a data scientist that it can seem like the absolute dream job.


What Getting A Job In Data Science Might Look Like

by Kristen Kehrer – 9 min read

I’ve read a number of articles stating how hard it was to get into Analytics and Data Science. This hasn’t been my experience, so I wanted to share.


Linear Algebra for Deep Learning

by Niklas Donges – 9 min read

The concepts of Linear Algebra are crucial for understanding the theory behind Machine Learning, especially for Deep Learning. It gives you a better intuition for how algorithms really work under the hood, which enables you to make better decisions.


A "weird" introduction to Deep Learning

by Favio Vázquez – 14 min read

There are amazing introductions, courses and blog posts on Deep Learning. I will name some of them in the resources sections, but this is a different kind of introduction.


Learning About Algorithms That Learn to Learn

by Cody Marie Wild – 13 min read

The premise of meta learning was an intoxicating one to me, when I first of heard it: the project of building machines that are not only able to learn, but are able to learn how to learn. The dreamed-of aspiration of meta learning is algorithms able to modify fundamental aspects of their architecture and parameter-space in response to signals of performance, algorithms able to leverage accumulated experience when they confront new environments.


Deep Learning Best Practices (1) – Weight Initialization

by Neerja Doshi – 7 min read

As a beginner at deep learning, one of the things I realized is that there isn’t much online documentation that covers all the deep learning tricks in one place. There are lots of small best practices, ranging from simple tricks like initializing weights, regularization to slightly complex techniques like cyclic learning rates that can make training and debugging neural nets easier and efficient.


Boost your data science skills. Learn linear algebra.

by hadrienj – 7 min read

The goal of this series is to provide content for beginners who wants to understand enough linear algebra to be confortable with machine learning and deep learning. However, I think that the chapter on linear algebra from the Deep Learning book is a bit tough for beginners.


NLP – Building a Question Answering model

by Priya Dwivedi – 7 min read.

I recently completed a course on NLP through Deep Learning (CS224N) at Stanford and loved the experience. Learnt a whole bunch of new things. For my final project I worked on a question answering model built on Stanford Question Answering Dataset (SQuAD).


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