AUTOENCODER AND ITS TYPES , HOW ITS USED, APPLICATIONS , ADVANTAGES AND DISADVANTAGES OF AUTOENCODER

AUTOENCODER AND ITS TYPES , HOW ITS USED, APPLICATIONS , ADVANTAGES AND DISADVANTAGES OF AUTOENCODER

1. Autoencoder

2. An autoencoder, also known as Autoassociator or Diabolo networks, is an artificial neural network employed to recreate the given input. It takes a set of unlabeled inputs, encodes them, and then tries to extract the most valuable information from them. Autoencoders are special neural networks that learn how to recreate the information given. They are useful for many tasks, like reducing the number of features in a dataset, extracting meaningful features from data, detecting anomalies, and generating new data.

3. How does autoencoder work for anomaly detection? The autoencoder learns a basic representation of the normal data and its reconstruction with minimum error. Therefore, the reconstruction error is used as an anomaly or classification metric. In addition, to detecting anomaly data from normal data, the classification of anomaly types has also been investigated

4. What are auto encoders in CNN? In an Autoencoder both Encoder and Decoder are made up of a combination of NN (Neural Networks) layers, which helps to reduce the size of the input image by recreating it. In the case of CNN Autoencoder, these layers are CNN layers (Convolutional, Max Pool, Flattening, etc.) What is continuous bag of words? The Continuous Bag of Words (CBOW) model is a neural network model that is used to learn word embeddings. Word embeddings are a type of representation for words that captures the semantic and syntactic relationships between words in a language.

5. What is the difference between undercomplete and overcomplete autoencoder? Based on the model configurations, AEs can be categorized. For instance, if the encoding layer has lower dimensionality than the input, the model is called undercomplete autoencoder. However, if the encoding layer has higher dimensionality, then the model is called overcomplete autoencoder

6. What are the two types of word embedding? Word2Vec has two neural network-based variants: Continuous Bag of Words (CBOW) and Skip-gram. 1. CBOW - The continuous bag of words variant includes various inputs that are taken by the neural network model.

7. What is the purpose of autoencoders? Autoencoders are neural network models primarily used for unsupervised learning tasks such as dimensionality reduction, data compression, and feature extraction. They learn to reconstruct the input data and capture its essential patterns, making them useful for anomaly detection and image-denoising tasks.

8. Architecture of autoencoder

9. What is the difference between autoencoder and convolutional autoencoder? During the training, both modes have the same loss function, optimizer, batch size and number of epochs for comparison purposes. However, the two architectures differ significantly as feedforward autoencoders have dense layers while convolutional autoencoders have convolutional and transposed convolutional layers.

10. What is the difference between bag of words and continuous bag of words? The Bag-of-Words model and the Continuous Bag-of-Words model are both techniques used in natural language processing to represent text in a computer-readable format, but they differ in how they capture context. The BoW model represents text as a collection of words and their frequency in a given document or corpus.

11. Why do we use bag of words? The most common practical application of the bag-of-words model is as a tool for feature generation. After you transform the text into a "bag of words", it becomes possible for you to calculate several different measures that can be used to characterize the text.

12. Why autoencoder is better than PCA? Autoencoded latent space may be employed for more accurate reconstruction if there is a nonlinear connection (or curvature) in the feature space. PCA, on the other hand, only keeps the projection onto the first principal component and discards any information that is perpendicular to it.

13. How many layers does the autoencoder have? Autoencoder consists of three layers including an input layer, a hidden layer, and the output layer. Input and output layers have an equal number of nodes in autoencoder.

14. What are the basics of autoencoders? Autoencoders are a special type of unsupervised feedforward neural network (no labels needed!). The main application of Autoencoders is to accurately capture the key aspects of the provided data to provide a compressed version of the input data, generate realistic synthetic data, or flag anomalies.