AI & Computer Science · AI Books
Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python (Sofien Kaabar)
LISTEN TO THIS EPISODE
Sofien Kaabar explains how to build Python models for financial time series, combining machine learning with technical and quantitative analysis.
The book develops the probability, statistics, linear algebra, and data-handling foundations needed for these models.
It progresses from regression and tree-based methods to neural networks, recurrent architectures, and deep reinforcement learning.
Backtesting, time-series cross-validation, regularization, and retraining help examine model performance and the risks of overfitting.
Market drivers, risk management, and behavioral biases place the modeling techniques within the wider practice of financial trading.
Part of Marginalia: books, papers, and ideas for learning and debate.