As a hands-on AI/ML professional and entrepreneur in this field, I hear from industry colleagues and my clients a continuous mantra about “we need more data, otherwise we can’t use AI to solve this problem / launch this feature / improve this process” Big players in the field are supporting…

Can you trade a cosine wave before the real markets? — There are many methodologies in algorithmic trading — from automated trade entry and close points based on technical and fundamental indicators to intelligent forecasts and decision making using complex maths and, of course, artificial intelligence. …

How to learn in the age of commoditized knowledge and commercialized research — Half a year ago I have agreed on a small adventure for myself. My university professor and thesis supervisor Luca di Persio offered me to join a teaching squad at a brand-new Data Science faculty at the University of Verona, my second alma mater. …

Strategies and tactics for independent experts — Remote work is a new normal today. The first months of “freedom” from the office were pretty fun but that job stays the job: dependent on a fixed salary / hourly rate, limited opportunities, risks of losing a warm place. Lots of us have tried to go solo, finding customers…

How to satisfy all stakeholders with a great product — Making buzzwords as “digitalization”, “innovation” and “big data” into the alive and profitable product is hard. And usually, it’s not the technology itself that fails, but the alignment between owners, managers, clients, employees, and sometimes the society. …

In the previous article, we have built experiments where we have learned how to approximate physical laws models with machine learning algorithms, which was a preamble for a “real” data generation process. …

GANs and other generative machine learning algorithms are still hyped and work fantastically with images, texts, and sounds. They’re able not only to generate data for fun but solve important theoretical issues and boost production ML pipelines. Unfortunately, typical today’s practical use case is limited to “fine-tune pre-trained StyleGAN2 for…

Understanding strategy risk and the probability of overfitting: small numbers that change everything — Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

Combinatorial and scenario-based backtesting from historical data and simulations — Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details. Let’s disassemble backtests and…