#62 Machine Learning & Data Science Challenge 62
Greetings.
I am a machine learning engineer based in India, possessing a sustained interest in machine learning since my undergraduate studies. I have completed Stanford University's machine learning course (Andrew Ng) via Coursera, and IBM's machine learning and deep learning curriculum. My current focus is on machine learning and data science projects, aiming to leverage my expertise for impactful, real-world problem-solving.
What are autoencoders?
An autoencoder is a neural network that has three layers:
An input layer, a hidden layer which is also known as the encoding layer, and a decoding layer.
This network is trained to reconstruct its inputs, which forces the hidden layer to try to learn good representations of the inputs.
An autoencoder neural network is an unsupervised Machine-learning algorithm that applies backpropagation, setting the target values to be equal to the inputs.
An autoencoder is trained to attempt to copy its input to its output.
Internally, it has a hidden layer that describes a code used to represent the input.
Autoencoder Components:
Encoder:
- In this, the model learns how to reduce the input dimensions and compress the input data into an encoded representation.
Bottleneck:
- In this, the layer contains the compressed representation of the input data. This is the lowest possible dimension of the input data.
Decoder:
- In this, the model learns how to reconstruct the data from the encoded represented to be as close to the original inputs as possible.
Reconstruction Loss:
- This method measures how well the decoder is performing and how close the output is related to the original input.
Types of Autoencoders:
Denoising Auto Encoder (DAE)
Sparse Auto Encoder (SAE)
Variational Auto Encoder (VAE)
Contractive Auto Encoder (CAE)
