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Roman Paolucci
Roman Paolucci

3.9K Followers

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Parallelizing Randomized Singular Value Decomposition using GPUs

Research in Applied Randomized Numerical Linear Algebra — Singular value decomposition is among the most powerful and widely used matrix decompositions in applied linear algebra. Though useful for extracting key features and patterns from data (e.g. Principal Component Analysis) it quickly becomes challenging to execute this algorithm on increasingly large matrices. Randomized numerical linear algebra offers an interesting…

Data Science

7 min read

Parallelizing Randomized Singular Value Decomposition using GPUs
Parallelizing Randomized Singular Value Decomposition using GPUs
Data Science

7 min read


Published in

Towards Data Science

·Pinned

How to Build a Neural Network for NLP Tasks with PyTorch and GPU

A framework for modeling text data using Google Colab — This article is meant to serve as a framework to solve natural language processing (NLP) problems using Python, neural networks, and GPU. Though a high level of math is required to understand everything herein, I wrote this article with the intent that an absolute beginner or seasoned veteran would get…

Data Science

7 min read

How to Build a Neural Network for NLP Tasks with PyTorch and GPU
How to Build a Neural Network for NLP Tasks with PyTorch and GPU
Data Science

7 min read


Published in

Towards Data Science

·Pinned

Q-Fin: A Python Library

A Working Library for Securities Pricing — Quantitative Finance In the past, I have written extensively on the topic of securities pricing… Black-Scholes Algorithmic Delta Hedging What Is Implied Volatility? Martingales and Markov Processes Stochastic Integrals Deriving the Black-Scholes Model Python for Pricing Exotics Volatility Trading 101 Risk-Neutral Portfolio Management Black-Scholes Option Pricing is Wrong

Python

3 min read

Q-Fin: A Python Library
Q-Fin: A Python Library
Python

3 min read


Jul 17, 2022

Cholesky Decomposition using Singular Value Decomposition

Increasing the Efficiency of Matrix Decompositions — As previously exhibited in our article on simulating correlated Brownian motions, the Cholesky decomposition is a very useful matrix decomposition. In practice we usually intend to impose correlation and covariance structures on large spaces, generating a common computational problem: time complexity. Generally speaking, the time complexity of the Cholesky decomposition…

Data Science

3 min read

Cholesky Decomposition using Singular Value Decomposition
Cholesky Decomposition using Singular Value Decomposition
Data Science

3 min read


Jun 23, 2022

Simulating Correlated Brownian Motions

A Guide to Generating Correlated Sample Paths — There are several reasons why one may wish to simulate multiple Brownian motions with an underlying correlation structure. Take for example a stylized fact of the market: the leverage effect…

Finance

3 min read

Simulating Correlated Brownian Motions
Simulating Correlated Brownian Motions
Finance

3 min read


Jun 10, 2022

Market Implied Volatility

Implications of Black-Scholes Pricing — Implied volatility can be a tricky topic. This article aims to provide an intuitive and clear understanding of what implied volatility is and how to compute it numerically using Python. First, let’s consider the problem space… Suppose we are trading an option contract on an underlying equity with a European…

Artificial Intelligence

5 min read

Market Implied Volatility
Market Implied Volatility
Artificial Intelligence

5 min read


Published in

Towards Data Science

·Aug 30, 2021

Singular Value Decomposition

Explanation, Derivation and Applications in Python — Introduction The linear algebra essential to data science, machine learning, and artificial intelligence is often overlooked as most introductory courses fail to display the big picture. Concepts such as eigendecomposition and singular value decomposition (SVD) are incredibly important from a practitioner's standpoint; they are the core of dimensionality reduction techniques including…

Machine Learning

7 min read

Singular Value Decomposition
Singular Value Decomposition
Machine Learning

7 min read


Published in

Geek Culture

·Jul 17, 2021

Quant Reading List

A Curated Selection of Books by Topic — This list comprises books that I have found helpful on my journey in quantitative finance. This list is by no means exhaustive, but I can personally vouch for the usefulness of each of these books as I have read them myself. Note: I am in no way affiliated with the…

Finance

2 min read

Quant Reading List
Quant Reading List
Finance

2 min read


Published in

Towards Data Science

·Jul 7, 2021

What is a Variational Autoencoder?

A Quickstart Guide to Generative Machine Learning with Code — Introduction Generative machine learning can be helpful in a variety of contexts. One, for example, is when some closed-form or quasi-closed-form solution is available but computationally expensive. In this case, a neural network may provide a timely solution with an acceptable degree of accuracy. Nevertheless, to train such a network, data…

Artificial Intelligence

7 min read

What is a Variational Autoencoder?
What is a Variational Autoencoder?
Artificial Intelligence

7 min read


Published in

Geek Culture

·Jun 8, 2021

Backtesting Quantitative Trading Strategies using Python and Pandas

With Real Strategies Developed by Quantitative Research — Quantitative Research Backtesting quantitative research prior to implementation in a live trading environment (see Algorithmic Trading with Python or Dynamic Algorithmic Trading Systems) is as essential as it is the fruit of a quantitative researcher’s labor. It’s critical to note that the outcome of a backtest is directly related to the quality…

Python

6 min read

Backtesting Quantitative Trading Strategies using Python and Pandas
Backtesting Quantitative Trading Strategies using Python and Pandas
Python

6 min read

Roman Paolucci

Roman Paolucci

3.9K Followers

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