Long Short Term Memory: Fundamentals and Applications for Sequence Prediction
About this ebook
Long short-term memory, often known as LSTM, is a type of artificial neural network that is utilized in the domains of deep learning and artificial intelligence. LSTM neural networks have feedback connections, in contrast to more traditional feedforward neural networks. This type of recurrent neural network, commonly known as an RNN, is capable of processing not only individual data points but also complete data sequences. Because of this property, LSTM networks are particularly well-suited for the processing and forecasting of data. For instance, LSTM can be used to perform tasks such as connected unsegmented handwriting identification, speech recognition, machine translation, speech activity detection, robot control, video game development, and healthcare.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Long short-term memory
Chapter 2: Artificial neural network
Chapter 3: Jürgen Schmidhuber
Chapter 4: Recurrent neural network
Chapter 5: Vanishing gradient problem
Chapter 6: Sepp Hochreiter
Chapter 7: Gated recurrent unit
Chapter 8: Deep learning
Chapter 9: Types of artificial neural networks
Chapter 10: History of artificial neural networks
(II) Answering the public top questions about long short term memory.
(III) Real world examples for the usage of long short term memory in many fields.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of long short term memory.
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