Posts

What are optimizers? Optimizers are mathematical functions or algorithms that aim to reduce the loss value of a model. In simpler terms, optimizers reduce the loss after forward propagation of a model which makes supervised models learn and become ‘smart’. When training a model, the objective is to minimize the loss/cost function by iteratively updating the model parameters(Weights and Biases) at each epoch. This approach aims in reducing the loss by finding the global minima for the loss function. ...

What are autoencoders? An autoencoder is a neural network that reconstructs a high dimensional input through a compressed lower dimension bottleneck. The idea for autoencoders is to take a high dimensional input, compress it to a lower dimension that represents the image’s features, and then reconstruct the image from the bottleneck. The autoencoder is essentially like a dimensionality reduction method like PCA (Principal Component Analysis) The idea was originally from the 1980s and was later promoted by Hinton & Salakhutdinov, 2006[1] ...