Data Science Austria

A Quant’s Intro to Portfolio Hedging

An intro to portfolio hedging, no finance background required… All analysis and programming efforts in this article can be found at: Poseyy/MarketAnalysis Portfolio Theory, Options Theory, & Quant Finance. Contribute to Poseyy/MarketAnalysis development by creating an… github.com This article is purely introductory and experienced options traders will have to wait … Read moreA Quant’s Intro to Portfolio Hedging

Working with TFRecords and tf.train.Example

www.tensorflow.org In this tutorial, I will go over how to save and read data in TFRecord format which is the recommended format by Tensorflow. We are going to use the protocol buffer message tf.train.Example. I will do everything in Tensorflow 2.0 so let’s install it first. !pip install tensorflow==2.0.0-beta1import tensorflow … Read moreWorking with TFRecords and tf.train.Example

How to Ease the Pain of Working with Imbalanced Data

A summary of methods and resources for creating a model using an imbalanced dataset You’ve finally collected and cleaned your data and have even completed some exploratory data analysis (EDA). All that hard work has finally paid off — time to start playing with models! However, you quickly realize that … Read moreHow to Ease the Pain of Working with Imbalanced Data

Neural Style Transfer and Visualization of Convolutional Networks

Create professional-looking artwork in 10 minutes using transfer learning. Likewise, we admire the story of musicians, artists, writers and every creative human because of their personal struggles, how they overcome life’s challenges and find inspiration from everything they’ve been through. That’s the true nature of human art. That’s something that … Read moreNeural Style Transfer and Visualization of Convolutional Networks

The proper way of handling mixed-type data. State-of-the-art distance metrics.

Photo by Annie Spratt on Unsplash Before we start, I would like to recommend to look at this paper if you want to get a more in-depth understanding of the algorithms I will talk about. My main goal here is to provide you with an intuitive understanding of those algorithms … Read moreThe proper way of handling mixed-type data. State-of-the-art distance metrics.

Deep Learning Book Series 3.4 and 3.5 Marginal and Conditional Probability

The sum rule allows to calculate marginal probability from joint probability. This content is part of a series about Chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as … Read moreDeep Learning Book Series 3.4 and 3.5 Marginal and Conditional Probability