Understanding and predicting sequential data, such as financial time series, speech, or text, requires a specialized approach to capture the time-dependent nature of the information. Traditional neural networks fall short as they don’t retain past information for future predictions. In this course, Recurrent Neural Networks (RNNs): Deep Learning for Sequences and Time Series, you’ll gain the ability to build and deploy RNN models that can predict and analyze time-based data with high accuracy. First, you’ll explore the fundamental concepts of RNNs, including how they process and remember information over time, which is crucial for understanding sequential data. Next, you’ll discover how to design and implement advanced RNN architectures such as Long Short-term Memory (LSTM) networks, which overcome the limitations of traditional RNNs by better handling long-range dependencies. Finally, you’ll learn how to fine-tune and evaluate your RNN models, ensuring they are robust, accurate, and ready to tackle real-world sequence prediction problems. When you’re finished with this course, you’ll have the skills and knowledge of RNNs needed to develop deep learning models that can forecast, generate, and interpret sequential data across various applications
Author Name: Pinal Dave
Author Description:
Pinal Dave is an SQL Server Performance Tuning Expert and independent consultant with over 22 years of hands-on experience. He holds a Master of Science degree and numerous database certifications. Pinal has authored 14 SQL Server database books and 81 Pluralsight courses. To freely share his knowledge and help others build their expertise, Pinal has also written more than 5,800 database tech articles on his blog at https://blog.sqlauthority.com.