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Weekly Selection – Aug 24, 2018

Deep Dive into Math Behind Deep Networks

By Piotr Skalski – 9 min read Nowadays, having at our disposal many high-level, specialized libraries and frameworks such as Keras, TensorFlow or PyTorch, we do not need to constantly worry about the size of our weights matrices or remembers formula for the derivative of activation function we decided to use.


Recent Advances for a Better Understanding of Deep Learning

By Arthur Pesah – 9 min read

This call for a better understanding of deep learning was the core of Ali Rahimi’s Test-of-Time Award presentation at NIPS in December 2017. By comparing deep learning with alchemy, the goal of Ali was not to dismiss the entire field, but "to open a conversation".


Use Kaggle to start (and guide) your ML/ Data Science journey – Why and How

By Nityesh Agarwal – 13 min read

Earlier, I wasn’t so sure. I would say something like do this course or read this tutorial or learn Python first (just the things that I did). But now, as I am going deeper and deeper into the field, I am beginning to realise the drawbacks of the approach that I took.


A "Data Science for Good" Machine Learning Project Walk-Through in Python

By William Koehrsen – 17 min read

Data science is an immensely powerful tool in our data-driven world. Call me idealistic, but I believe this tool should be used for more than getting people to click on ads or spend more time consumed by social media.


Everything you need to know about AutoML and Neural Architecture Search

By George Seif – 7 min read

AutoML and Neural Architecture Search (NAS) are the new kings of the deep learning castle. They’re the quick and dirty way of getting great accuracy for your machine learning task without much work. Simple and effective; it’s what we want AI to be all about!


Generative Adversarial Nets and Variational Autoencoders at ICML 2018

By Agrin Hilmkil – 11 min read

Generative models classically describe models of joint distribution p(x, y) with data (x) and labels (y). For this context, however, generative models will be taken to mean those with mechanisms to sample from the (approximate) distribution of data X to produce new samples x ~ X


Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management

By Hamaad Shah – 18 min read

We will explore the use of Bidirectional Generative Adversarial Networks (BiGAN) for market risk management: Estimation of portfolio risk measures such as Value-at-Risk (VaR).


Measuring Model Goodness

By Ajay Thampi – 9 min read

Data and AI are transforming businesses worldwide from finance, manufacturing and retail to healthcare, telecommunications and education. At the core of this transformation is the ability to convert raw data into information and useful, actionable insights.


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