A Gentle Introduction to Long Short-Term Memory Networks by the Experts
Last Updated on February 20, 2020
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems.
This is a behavior required in complex problem domains like machine translation, speech recognition, and more.
LSTMs are a complex area of deep learning. It can be hard to get your hands around what LSTMs are, and how terms like bidirectional and sequence-to-sequence relate to the field.
In this post, you will get insight into LSTMs using the words of research scientists that developed the methods and applied them to new and important problems.
There are few that are better at clearly and precisely articulating both the promise of LSTMs and how they work than the experts that developed them.
We will explore key questions in the field of LSTMs using quotes from the experts, and if you’re interested, you will be able to dive into the original papers from which the quotes were taken.
Kick-start your project with my new book Long Short-Term Memory Networks With Python, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.