query: Why do we need Auto-encoder? How is it useful in real life implementation?
positive_passages.docid: 93143
positive_passages.text: Why do we need autoencoders?
negative_passages.docid: 85010
negative_passages.text: What're the differences between PCA and autoencoder?
negative_passages.docid: 109966
negative_passages.text: How does the Adam method of stochastic gradient descent work?
negative_passages.docid: 149875
negative_passages.text: Why does increasing the number of features reduce performance?
negative_passages.docid: 59364
negative_passages.text: Why should the data be shuffled for machine learning tasks
negative_passages.docid: 57060
negative_passages.text: Is normalizing the features always good for classification?
negative_passages.docid: 94250
negative_passages.text: Comprehensive list of activation functions in neural networks with pros/cons
negative_passages.docid: 147911
negative_passages.text: Why we use inner classes?
negative_passages.docid: 9663
negative_passages.text: Is it a good practice to always scale/normalize data for machine learning?
negative_passages.docid: 70549
negative_passages.text: Why use JPEG instead of RAW?
negative_passages.docid: 61480
negative_passages.text: Why in Variational Auto Encoder (Gaussian variational family) we model $\log\sigma^2$ and not $\sigma^2$ (or $\sigma$) itself?
negative_passages.docid: 31297
negative_passages.text: Why do we use ReLU in neural networks and how do we use it?
negative_passages.docid: 22078
negative_passages.text: Advantages of combined PRNGs
negative_passages.docid: 88957
negative_passages.text: Why do we use Gaussian distributions in Variational Autoencoder?
negative_passages.docid: 26416
negative_passages.text: Active versus passive transformations
negative_passages.docid: 75838
negative_passages.text: Understanding batch normalization
negative_passages.docid: 137086
negative_passages.text: Can Convolution Neural Network be useful with encoded categorical features?