Deep Learning: UNIT 4 : Autoencoders

-

UNIT IV

Autoencoders

1.     Denoising Autoencoders

2.     Sparse Autoencoders

3.     Deep Autoencoders

4.     Variational Autoencoders

5.     GANS 


👉Autoencoders Notes

👉Autoencoders PPTs


Comments

Post a Comment

Popular posts from this blog

VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM

-
Image
  VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM   •         The key idea in the CANDIDATE-ELIMINATION algorithm is to output •         a description of the set of all hypotheses consistent with the training examples Note difference between definitions of consistent and satisfies ·        An example x is said to satisfy hypothesis h when h(x) = 1, regardless of whether x is a positive or negative example of the target concept . ·        An example x is said to consistent with hypothesis h iff h(x) = c(x) The LIST-THEN-ELIMINATION algorithm The LIST-THEN-ELIMINATE algorithm first initializes the version space to contain all hypotheses in H and then eliminates any hypothesis found inconsistent with any training example . ___________________________________________________________________________ 1. VersionSpace c...
Machine Learning

Deep Learning: UNIT-1 : Deep Learning Fundamentals- Short Answer Questions

-
  UNIT-1 Deep Learning: Fundamentals Short Answer Questions ----------------------------------------------------------------------------------------------------------------------------- 1.      Define Artificial Neural Network. 2.      Define Neuron. 3.      List the operations performed by ANN layers. 4.      List the different applications of Deep Learning. 5.      Define Deep Learning. 6.      List the different applications of Artificial Neural Network. 7.      List the Building Block of Neural Networks. 8.      Define Dense layer. 9.      What is loss function. 10.   Identify the different layers in ANN. 11.   Explain Forward Pass. 12.   Explain Backward Pass. 13.   List the different optimizers. 14.   How to overcome vanishing and exploding grad...
Machine Learning

Machine Learning 1: UNIT 3 : Support Vector Machines MCQs

-
UNIT 3 Support Vector Machines MCQs  ----------------------------------------------------------------------------------------------------------------------------- 1. A Support Vector Machine can be used for A.     Performing linear or nonlinear classification B.     Performing regression C.     For outlier detection D.     All of the above Ans: D 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? Top of Form True False Ans: A 3. Support Vector Machines are not sensitive to feature scaling A.     Top of Form True False Ans: B 4. If we strictly impose that all instances be off the street and on the right side, this is called  Soft margin classification  Hard margin classification  Strict margin classification  Loose margin classification Ans...
Machine Learning