Long Short-Term Memory usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. LSTMs are explicitly designed to avoid the long-term dependency problem. Remembering information for long periods of time is their default behavior. All recurrent neural networks have the form of a chain of repeating modules of a neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer. LSTMs also have this chain like structure, but the repeating module has a different structure. The key to LSTMs is the cell state which is acting like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to flow along it unchanged. The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates. The first step in LSTM is to decide what information it is going to throw away from the cell state. This decision is made by a sigmoid layer called the “forget gate layer.” The next step is to decide what new information we’re going to store in the cell state. The last step is a decision on output.
Was the above useful? Please share with others on social media.
Recommended reading list:
|Data Science from Scratch: First Principles with Python
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases