Neural Finance: Deep Learning Architectures for Predicting Markets and Managing Risk
Unlock the Future of Finance with Deep Learning
In an age where markets move faster than human intuition can follow, Neural Finance delivers a breakthrough roadmap for leveraging deep learning to outpace traditional investment strategies. This comprehensive guide bridges the gap between cutting-edge AI architectures and real-world financial application, offering readers a powerful toolkit to forecast trends, manage risk, and engineer intelligent trading systems.
From convolutional neural networks for time-series analysis to transformer models in portfolio optimization, Johann Strauss demystifies the science behind the models and equips financial professionals, quants, and developers with practical implementation strategies.
Inside this book:
How to use LSTMs and GRUs for sequential market prediction
Applying autoencoders for anomaly detection and risk control
Building reinforcement learning agents for algorithmic trading
Integrating transformers to interpret macroeconomic signals
Using real market datasets to train and validate neural models
Whether you're a financial analyst looking to future-proof your skills, or a data scientist venturing into capital markets, Neural Finance is your definitive guide to mastering AI in the financial arena.
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