What is LSTM with Example | Long Short-Term Memory | Recurrent Neural Networks | SImplilearn

In this video on What is LSTM with example,we will explore Long Short-Term Memory (LSTM) networks, a crucial part of deep learning. We begin by understanding what LSTM is and why it is essential for processing sequential data. Next, we delve into Recurrent Neural Networks (RNNs), laying the foundation for how LSTMs address the limitations of traditional RNNs. Moving forward, we will examine the types of gates in LSTM—input, forget, and output gates—that control the flow of information and enable LSTMs to maintain long-term dependencies. We will also discuss some key applications of LSTMs, highlighting their significance in fields such as natural language processing, time series prediction, and speech recognition. Finally, we will conclude with how LSTM works with an example, where we will predict the next word in the sentences. This demonstration will provide a step-by-step guide to building and training an LSTM model, showcasing its power and versatility in machine learning. By the end of this video, you will have a comprehensive understanding of LSTMs and their applications, equipped with the knowledge to implement them in your projects.

Below are the topics covered in this Long short-term memory video:-
00:00 Introduction To LSTM?
01:35 What Is LSTM?
03:02 What Is RNN(Recurrent Neural network)
03:42 Types of LSTM Gates
05:12 LSTM Applications
05:47 How LSTM works with Example?

✅What is LSTM and why is it used?
LSTMs are long short-term memory networks that use (ANN) artificial neural networks in the field of artificial intelligence (AI) and deep learning. In contrast to normal feed-forward neural networks, also known as recurrent neural networks, these networks feature feedback connections.

✅What is the difference between LSTM and RNN?
LSTM differs from RNN as it includes a forget gate, enabling the network to discard information that is no longer relevant. This feature enhances LSTM networks' efficiency in learning long-term dependencies. Additionally, these neural networks can manage input sequences of any length.

✅What are the 4 gates of LSTM?
It is a unit structure of LSTM, including 4 gates: input modulation gate, input gate, forget gate and output gate. We describe recurrent neural networks (RNNs), which have attracted great attention on sequential tasks, such as handwriting recognition, speech recognition and image to text.

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