Introduction To Autoencoders In Machine Learning.

Underfitted · Beginner ·📐 ML Fundamentals ·3y ago
Autoencoders are neural networks designed in a way they can learn any existing structure in a dataset. They create a compact representation of the data we can leverage later in different applications. Some applications where you can leverage autoencoders: anomaly detection, image denoising, information retrieval, imputation, feature extraction, and dimensionality reduction problems. Convolutional autoencoder for image denoising example: https://keras.io/examples/vision/autoencoder/ 🔔 Subscribe for more stories: https://www.youtube.com/@underfitted?sub_confirmation=1 📚 My 3 favorite Machi…
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