LSTM – Long Short Term Memory Architecture

LSTM – Long Short-Term Memory is an optimized RNN for gradient issues. RNNs can model sequences thanks to cyclic connections, unlike feedforward neural networks.

Models successful at sequence labeling and prediction. Despite being widely used, RNNs are underutilized in speech recognition, mainly for minor phone recognition tasks. New RNN architectures use LSTM to improve speech recognition training for large lexicons. In this blog post will evaluate LSTM model performance with different parameters and settings. How and why LSTM shows superior speech recognition and fast convergence with compact models.

AILabPage Define Artificial Neural Networks as

Deep learning which is a subset of machine learning utilizes interconnected nodes to create a layered structure that emulates (try to) the operation of the human brain through connected neurons. Artificial neural networks strive to achieve precision in intricate duties such as identifying faces and condensing texts.

Deep Learning

Deep learning, in short, is going much beyond machine learning and its algorithms that are either supervised or unsupervised. In DL, it uses many layers of nonlinear processing units for feature extraction and transformation.

It has revolutionized today’s industries by demonstrating near human-level accuracy in certain tasks. tasks like pattern recognition, image classification, voice or text decoding, and many more. Self-driving cars are one of the best examples and biggest achievements so far.

The hype and optimism surrounding Artificial Intelligence have led to a widespread real “fake news disease” and misbelief about neural networks, resembling a prevalent issue of misinformation, as people mistakenly assume they function similarly to the human brain.

Artificial Neural Network – Outlook

Neural networks were intentionally crafted to emulate biological neural networks and serve as algorithms dedicated to this specific objective. The basic concept of neural networks relies on connecting neurons according to the unique arrangement of the network. Initially, the aim was to create an artificial system with the ability to function like the human brain sadly its far from the reality.

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Deep Learning – Introduction to Artificial Neural Networks

How Neural Network Algorithms Works: An Overview

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In brief, Artificial Neural Networks (ANNs) are mathematical entities that were initially formulated to mimic biological neurons, although the degree of approximation remains open for further inquiry. Researchers are endeavouring to unravel the potential of a brain-computer interface. The task of simulating the human brain with AI is a formidable undertaking and is unlikely to be achieved within the next half-century or so.

Long Short Term Memory-LSTM Outlook

The LSTM algorithm overcomes the challenges of processing sequential data. Calling LSTM as an advanced RNNs with with extra complexity is not wrong. LSTMs are known for their ability to capture sequential data by forming enduring associations among successive events thus they excel in processing sequential data with long-term dependencies.

LSTM is a popular technique used in various disciplines, such as sentiment analysis, language generation, speech recognition, and video analysis. The system includes memory units and mechanisms to determine important information for long-term storage. LSTMs includes a distinctive design methodology used for recurrent neural networks (RNNs) with the objective of surmounting the limitations of conventional RNNs in identifying complex patterns within sequential data.

The architecture of LSTM has special memory cells and gating mechanisms that enable the model to capture and retain long-term dependencies. LSTM components and functions breakdown:

1. Memory Cell: LSTM unit’s core is memory cell. It maintains and updates data for a long time. It has a memory function for long-term retention.
2. Input Gate: The input gate controls memory cell retention of information. Calculation relies on current input and previous state.
3. Forget Gate:The forget gate deletes unnecessary details from the memory cell. Evaluates the importance of stored information in the memory cell by considering both the current input and the prior hidden state.
4. Output Gate: The output gate controls data discharge and affects the following states: The process of deciding the data to transfer considers both the present input and the preceding hidden state.
5. Cell State: The output gate controls data discharge and affects the following states: The process of deciding the data to transfer considers both the present input and the preceding hidden state.
6. Hidden State: LSTM output is in a hidden state. It relays deliberate information to follow-up intervals or interconnected layers of the neural network. Cell state and output gate affect the hidden state.

By working together in harmony, the individual parts come together to enhance the Long Short-Term Memory (LSTM) design’s ability to learn and retain information over long periods of time. This ultimately results in its capability to effectively handle and integrate sequential data with consistent connections.

The LSTM technique in RNNs aims to tackle the issue of gradient vanishing through the use of input, forget, and output gates that regulate the information flow via gating mechanisms. Leveraging advanced memory cells and gating mechanisms, LSTM networks exhibit outstanding aptitude for carrying out diverse sequential assignments, encompassing, but not limited to, functions such as language modeling, speech recognition, and time series forecasting.

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Some Examples of Neural Networks

There are several kinds of Neural Networks in deep learning. Some of them we have defined in our previous blog posts.

• Recurrent Neural Networks
• Generative Adversarial Networks
• Convolutional Neural Networks.

The human brain is an impressive feat of cognitive engineering, giving us the upper hand when it comes to coming up with original ideas and concepts. We’ve even managed to create the wheel—something that not even our robot friends could do! This shows just how far we’ve come in terms of evolution, proving that humans are true masters of invention.

Points to Note:

Uh oh, it’s time to figure out when to use which “machine learning algorithm”—a tricky decision that can really only be tackled by the experts! So if you think you’ve got the right answer, take a bow and collect your credits! And don’t worry if you don’t get it right; this next post will walk us through neural networks’ “neural network architecture” in detail.

Books Referred & Other material referred

• Open Internet research, news portals and white papers reading
• Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.
• Self-Learning through Live Webinars, Conferences, Lectures, and Seminars, and AI Talkshows

Feedback & Further Question

Do you have any questions about AI, machine learning, data science, or big data analytics? Leave a question in a comment or ask via email. I will try my best to answer it.

Conclusion –  Undeniably, ANN’s and the human brain are not the same, and function and working are also very different. We have seen in the post above that ANNs don’t create or invent any new information or facts, but the human brain does. ANN helps us make sense of what’s already available in the hidden format. ANN takes an empirical approach to a massive amount of data to give the best and most accurate results.

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