Build Your First Autoencoder in Keras | Easy Guide

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Autoencoders are a type of artificial neural network used for unsupervised learning. They are designed to learn efficient representations of data, typically for the purpose of dimensionality reduction or feature learning. An autoencoder consists of two main parts: an encoder that compresses the input into a lower-dimensional representation, and a decoder that reconstructs the original input from this compressed representation. By training the network to minimize the difference between the input and the reconstructed output, autoencoders can discover important features and patterns within the data. They are widely used in applications such as image compression, denoising, and anomaly detection.

In this video, we'll take you through a step-by-step guide on how to build, train, and evaluate autoencoders in Python with Keras.

0:00 What is an autoencoder?
3:50 Processing the data
7:53 Building the autoencoder
11:14 Training the autoencoder
12:10 Testing the autoencoder

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[yas-h]

hi, thanks for the video, keep it up. just one question why did you put predicted results into the model again? what I understood from the video is that model first encode the image then decode it so there is no need for second prediction, what am i missing?

[LaPtaVerdad]

Man absolutely loving your videos
I have ZERO knowledge of programming (toyed with it in C like a decade ago xD) and you make it so easy and understandable that im coding man!!!

[UCSRRBalaji]

Hi, i have the PTB XL ECG dataset that contains waveform signals and i convert that all signals into 1d numpy array, now I'm implement the autoencoder model in deep learning. It is efficient or not and give some tips me furthermore process