Page 336 - Ai_417_V3.0_C9_Flipbook
P. 336
Some of the examples of RNNs are as follows:
• It can generate novel text in the style of a specific author or genre, like creating new sentences that mimic the
style of Shakespeare or generating dialogue for a chatbot.
• It can predict the next character or word in a sequence, like autocomplete features in text editors and predictive
text input on smartphones.
• It can be used to predict future values in a time series, such as stock prices or weather data, by learning patterns
from historical data.
Autoencoders (AEs)
These are neural networks that have been trained to learn a compressed representation of data. They work by
compressing the data into a lower-dimensional form (encoding) and then decompressing it back to its original
form (decoding). This process helps the network learn the most important features of the data.
Latent Space
Input Encoder Decoder Output
Some of the examples of AEs are as follows:
• It can help in cleaning up noisy images to produce clear and highly realistic samples.
• It can help in compressing high-resolution images for efficient storage and transmission.
• It can create artistic images based on learned features from famous paintings.
• It can help in drug discovery by learning and generating molecular structures that have desirable properties.
Similarities and Differences between AEs and VAEs
The similarities and differences between Autoencoders (AEs) and Variational Autoencoders (VAEs) are as follows:
Similarities
• Both AE and VAE are neural network architectures that are used for Unsupervised Learning
• Both AE and VAE consist of an encoder and a decoder network. The encoder maps the input data to a latent
representation, and decoder maps the latent representation back to the original data.
• Both AE and VAE can be used for tasks such as dimensionality reduction, data generation, and anomaly detection.
Differences
AE VAE
Basic Function Neural network model that learns to Similar to AE but incorporates
encode input data into a compressed probabilistic elements to learn a latent
representation and then decode it space representation of input data.
back to the original data.
334 Touchpad Artificial Intelligence (Ver. 3.0)-IX

