Machine Learning for Economics and Finance in TensorFlow 2

This book addresses the integration of machine learning in economics and finance, covering various models and techniques. It provides practical examples using TensorFlow 2, targeting students and professionals in the field.

"Machine Learning for Economics and Finance in TensorFlow 2" explores the integration of machine learning techniques within the field of economics and finance. Historically, the adoption of machine learning in academic economics has been slow due to the discipline's focus on establishing causal relationships through simple statistical models. In contrast, machine learning emphasizes prediction, often overlooking causality and simplicity. This discrepancy has created a need for a comprehensive resource that bridges the gap for students, academics, and professionals seeking to understand how machine learning can be applied to economic and financial issues.

The book delves into a range of empirical problems where machine learning can provide valuable insights. It covers various models, including discriminative deep learning models like DNNs, CNNs, LSTMs, and DQNs, as well as generative models such as GANs and VAEs, along with tree-based methods. Additionally, it examines how traditional empirical methods in economics intersect with machine learning techniques, addressing topics such as regression analysis, natural language processing, and dimensionality reduction.

Structured around practical examples, the book aims to simplify complex concepts, enabling readers to apply TensorFlow to solve theoretical models in economics and finance. By guiding readers through the process of defining, training, and evaluating machine learning models, it equips them with the skills to tackle real-world economic problems. This resource is particularly beneficial for students, data scientists, economists in both public and private sectors, and social scientists looking to enhance their understanding of machine learning applications in their fields.