Discussing the article: "Data Science and ML (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal"

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Check out the new article: Data Science and ML (Part 22): Leveraging Autoencoders Neural Networks for Smarter Trades by Moving from Noise to Signal.

In the fast-paced world of financial markets, separating meaningful signals from the noise is crucial for successful trading. By employing sophisticated neural network architectures, autoencoders excel at uncovering hidden patterns within market data, transforming noisy input into actionable insights. In this article, we explore how autoencoders are revolutionizing trading practices, offering traders a powerful tool to enhance decision-making and gain a competitive edge in today's dynamic markets.

Let us dissect autoencoders and observe what are they made up of and what makes them special.

At the core of an autoencoder, there is an artificial neural network which consists of three parts.

1. The Encoder
2. The Embedding vector/latent layer
3. The Decoder

The left part of the neural network is called an encoder, Its job is to transform the original input data into lower dimensional representation.

The middle part of the neural network is called the latent layer or Embedding vector, its role is to compress the input data into lower dimensional data, This layer is expected to have fewer neurons than both the encoder and the decoder.

The right part of this neural network is called the decoder, Its job to to recreate the original input using the output of the encoder In other words, It tries to reverse the encoding process.

Author: Omega J Msigwa