This comprehensive course on Long Short-Term Memory (LSTM) equips you with the skills to build advanced sequence models for time series forecasting and natural language processing. Begin by understanding the fundamentals of Recurrent Neural Networks (RNNs) and how LSTM addresses vanishing gradient issues. Dive into the LSTM architecture—learn the functions of forget, input, and output gates and how they manage memory over time. Progress to practical applications across industries including finance, healthcare, and AI-driven chat systems. Gain hands-on experience through guided demos that walk you through real-world LSTM implementations.
Introduction to Long Short Term Memory (LSTM) Training
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Beginner level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Understand how LSTM networks overcome RNN limitations in sequence modeling
Learn the structure and function of LSTM gates: forget, input, and output
Apply LSTM to real-world tasks like time series forecasting and NLP
Build and evaluate LSTM models through step-by-step practical demos
Skills you'll gain
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Recently updated!
June 2025
Assessments
3 assignments
Taught in English
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There is 1 module in this course
Master the fundamentals of Long Short-Term Memory (LSTM) networks in this hands-on module. Begin with the basics of RNNs and understand how LSTM overcomes their limitations. Explore LSTM architecture, including forget, input, and output gates. Learn real-world applications in time series, NLP, and more through interactive demos designed to reinforce practical LSTM implementation.
What's included
8 videos1 reading3 assignments
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Frequently asked questions
LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) designed to capture long-range dependencies in sequential data. It overcomes the limitations of traditional RNNs by using gate mechanisms to retain or forget information.
The main gates in an LSTM are the Forget Gate, Input Gate, Output Gate, and sometimes a Cell Gate (though typically counted as part of the Input gate logic). These gates regulate the flow of information, enabling the model to manage memory effectively.
The LSTM technique involves using gated cells to control the flow of information over time, making it ideal for tasks like time series prediction, language modeling, and speech recognition where context from past inputs is important.
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