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What are Autoencoders ?
Autoencoders is a type of artificial neural network that is used for data encoding in unsupervised learning. It is a specific neural network where the input and output are the same. The aim of an autoencoder is to convert the input data into lower dimensional code and then reconstruct the final output in the original form same as the input.
By training the network to disregard inconsequential data, the autoencoder learns a representation (encoding) for a set of data, generally for dimensionality reduction.
Autoencoders are used in many problems like facial recognition, feature detection, anomaly detection etc. It also randomly generates the new data that is similar to the original data.
Basic Architecture
Autoencoder has three parts :
Encoder
Bottleneck
Decoder
Encoder : Encoder compressed the input data and generate the code
Bottleneck : This is the most significant part of the network because it holds the compressed knowledge representations.
Decoder : Decoder decompressed the encoded data into original form from encoding.
Autoencocer Homework Help
Artificial intelligence is a popular course that has been introduced in educational institutions and universities and machine learning and deep learning is the field of artificial intelligence. Autoencoder is a deep learning algorithm. Deep learning is about computers learning to think using structures modeled on the human brain. It helps humans to save time, effort and money.
Students who are pursuing the Machine learning or Artificial intelligence course face difficulty to complete the homework on time due to a busy schedule. Students need to deal with a lot of complex terminologies to solve Machine learning Homework Assignments or Artificial Homework Assignments. They look for help. Programming often gives a tough time to students struggling to meet their deadline for the homework or assignments.
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