MACHINE LEARNING UNIT-5(A) PPT's (Analytical Learning)

-

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

About Deep Learning

-
 Deep Learning UNIT I Deep Learning: Fundamentals, Introduction, Building Block of Neural Networks, Layers, MLPs, Forward pass, backward pass, class, trainer and optimizer, The Vanishing and Exploding Gradient Problems, Difficulties in Convergence, Local and Spurious Optima, Preprocessing, Momentum, learning rate Decay, Weight Initialization, Regularization, Dropout, SoftMax, Cross Entropy loss function, Activation Functions. 👉 Deep Learning: UNIT 1 (A) Notes: Deep Learning: Fundamentals Part 1 Notes 👉 Deep Learning: UNIT 1 (A) PPTs: Deep Learning Fundamentals Part 1 PPTs 👉 Deep Learning: Unit 1 (B) Notes: Deep Learning Fundamentals Part 2 Notes 👉 Deep Learning: UNIT 1 (B): Deep Learning: Fundamentals Part2 PPTs 👉 Deep Learning: UNIT 1: Deep Learning - Fundamentals: Long Answer Questions 👉 Deep Learning: UNIT 1: Deep Learning - Fundamentals : Short Answer Questions   UNIT II CNN: Introduction, striding and padding, pooling layers , stru...
Machine Learning