A Journey Through Time Series, RNNs, LSTMs to the Pinnacle of Attention Mechanisms

Author(s): Shenggang Li

Originally published on Towards AI.

Bridging the Gap Between Theory and Application in Predictive Modeling
Photo by Alina Grubnyak on Unsplash

Approaching Transformers and advanced language models for the first time can indeed seem formidable. My post here is to simplify this complexity into something more approachable. Here’s an outline of what I’ll cover:

Starting: I’ll begin with the basics of encoding and decoding — essential concepts at the heart of language processing.

Step further: Then, I’ll discuss the basics of traditional models such as the time series autoregression model, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) units, important for capturing how sequential data is processed.

Game changer: The focus will shift to the Transformers. These models have significantly changed the manner of natural language processing, offering new possibilities and efficiencies.

Deep Dive into Attention: I’ll learn the attention mechanism, a key component that powers Transformers and allows for their impressive capabilities in handling complex language tasks.

For readers already familiar with foundational topics such as encoding, time series analysis, and traditional neural network architectures, feel free to jump ahead to the sections focusing on Transformers and the attention mechanism.

‘’Machine learning can be regarded as the processes of encoding and decoding. Understanding encoding and decoding is crucial for mastering transformers and attention mechanisms, shedding light on their core role… Read the full blog for free on Medium.

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Published via Towards AI