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Autoencoders

INTRODUCTION TO AUTOENCODERS Welcome to the fascinating world of autoencoders! These neural networks are a crucial component of unsupervised learning, and they excel at learning efficient representations of data without the need for labels. PARTS OF AUTOENCODERS Autoencoders consist of two parts: an encoder and a decoder. The encoder processes input data, creating a compressed representation in a latent space, while the decoder reconstructs the original input from this representation. In unsupervised learning, where data lacks labels, autoencoders shine by capturing essential characteristics and structures through encoding and decoding. They are particularly useful for dimensionality reduction and feature extraction, transforming high-dimensional data into a more manageable form. ARCHITECTURE OF AUTOENCODERS The unique architecture of autoencoders includes a bottleneck, which encourages data compression by reducing the number of neurons in the latent space. This...

Convolutional Neural Networks (CNN)

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Neuroevolution of Augmenting Topologies

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