Stock Prediction With Deep Learning
Download Stock Prediction With Deep Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Stock Prediction With Deep Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Deep Learning Tools for Predicting Stock Market Movements
DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
Stock Prediction with Deep Learning
For centuries, human beings have tried to predict the future, whether it be NBA playoffs, weather, or elections. In this book, we tackle the common misconception that the stock market cannot be predicted, and build a stock prediction algorithm to beat the stock market, using Deep Learning, Data Analysis, and Natural Language Processing techniques.If you're new to Artificial Intelligence and Python, and are curious to learn more, this is a great book for you! Industry experts also have plenty to learn from the variety of methods and techniques used in data collection and manipulation.ABOUT THE AUTHOREthan Shaotran is an AI developer, researcher, and author of "Stock Prediction with Deep Learning". He is the founder of Energize.AI, where he built a financial stock prediction algorithm that outperformed the stock market in 2017. He is currently working on a thought experiment series to raise awareness on AI-related societal challenges within the AI community, regarding regulation and potential moral hazards, as well as autonomous vehicle driving software. Ethan has studied Economics and AI courses from Harvard, Stanford, and USF, is an affiliate with the Harvard Kennedy School's AI Initiative and is a member of the Association for Computing Machinery.
Deep Learning Theory and Applications
This book consitiutes the refereed proceedings of the 4th International Conference on Deep Learning Theory and Applications, DeLTA 2023, held in Rome, Italy from 13 to 14 July 2023. The 9 full papers and 22 short papers presented were thoroughly reviewed and selected from the 42 qualified submissions. The scope of the conference includes such topics as models and algorithms; machine learning; big data analytics; computer vision applications; and natural language understanding.