The application of Artificial Intelligence (AI) in Finance has grown in leaps and bounds in the past decade. The massive growth of quantum computing techniques, AI&ML tools, and the availability of big data have tremendous potential for new disruptions that can revolutionize the financial landscape globally. AI in worldwide banking is estimated to grow exponentially between 2021 and 2030 with a CAGR of 32.6% and a market valuation of $64.03 billion by 20301. The global financial markets are so dynamic that they are not only influenced by a country's macroeconomic indicators and the firms’ fundamentals but by various other factors, namely investors’ psychology and market sentiments, geopolitical events, climate change, and environmental factors. There is an ocean of data generated by financial markets given the digital revolution, implementation of algorithmic trading and automation of trading systems. The Deep Learning (DL) models excel at recognizing patterns in both structured and unstructured datasets of large data volumes and big data, which may not be possible for human analysts to analyze. Various stakeholders to stock markets, namely, the investors, traders, banks and other financial institutions, hedgers, regulators, credit rating agencies, and others, can gain deep insights into the real-time data and predict future market movements, take advantage of market trends, leverage profitable trading opportunities, hedge risk, and many more. This book chapter aims to enlighten the readers on Deep Learning techniques for analyzing structured and unstructured financial data by taking the example of the Indian stock market to create Deep Learning models. The readers should be able to achieve the following learning objectives at the end of this chapter. Learning objectives: (1) understand DL tools and techniques outlined in the chapter, (2) predict the market volatility, assess market risk or price volatility, and (3) predict the market crash.