Machine Learning

UNIT V  Transfer Learning  Types  Methodologies  Diving into Transfer Learning  Challenges  

  UNIT V  Transfer Learning  1. Types  2. Methodologies  3. Diving into Transfer Learning  4. Challenges 👉 Transfer learning Notes 👉 Transfer Learning PPTs

UNIT IV Autoencoders 1.      Denoising Autoencoders 2.      Sparse Autoencoders 3.      Deep Autoencoders 4.      Variational Autoencoders 5.      GANS   👉 Autoencoders Notes 👉 Autoencoders PPTs

UNIT V  Transfer Learning  1. Types  2. Methodologies  3. Diving into Transfer Learning  4. Challenges   Reference: Dipanjan Sarkar, Raghav Bali, “Transfer Learning in Action”, Manning Publications, 2021

UNIT IV  Autoencoders  1. Denoising Autoencoders  2. Sparse Autoencoders  3. Deep Autoencoders  4. Variational Autoencoders  5. GANS   Reference: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, SECOND EDITION, O’Reilly

UNIT IV  Autoencoders   1. Denoising Autoencoders  2. Sparse Autoencoders  3. Deep Autoencoders  4. Variational Autoencoders  5. GANS   Reference: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, SECOND EDITION, O’Reilly

  NN : Forward and Back Propagation Q1. Sigmoid and softmax functions Which of the following statements is true for a neural network having more than one output neuron ? Choose the correct answer from below: A.     In a neural network where the output neurons have the sigmoid activation, the sum of all the outputs from the neurons is always 1. B.     In a neural network where the output neurons have the sigmoid activation, the sum of all the outputs from the neurons is 1 if and only if we have just two output neurons. C.     In a neural network where the output neurons have the softmax activation, the sum of all the outputs from the neurons is always 1. D.     The softmax function is a special case of the sigmoid function Ans: C Correct option :  In a neural network where the output neurons have the softmax activation, the sum of all the outputs from the neurons is always 1. Explanation : ...