Deep Learning for Finance' delves into the cutting-edge realm of deep neural networks (DNNs) tailored specifically for financial applications. Throughout this course, doctoral candidates will learn to construct and train DNNs, exploring crucial architectural parameters such as network architecture and activation functions. Candidates will gain hands-on experience in implementing vectorized neural networks, optimizing for efficiency and scalability in financial data processing. Furthermore, you will analyze variance in deep learning applications within the financial domain, addressing challenges related to data heterogeneity, model interpretability, and algorithmic stability. By the end of this course, candidates will be equipped with the expertise to harness the power of deep learning in revolutionizing digital finance, paving the way for innovative solutions in risk assessment, portfolio management, and algorithmic trading.