Course Instructor
Deep Learning Practical
FREE
Practical
Practical 2 Aim: Solving XOR problem using deep feed forward network.
Practical 3 Aim: Implementing deep neural network for performing binary classification task.
Practical 4 Aim: a) Using deep feed forward network with two hidden layers for performing multiclass classification and predicting the class.
Practical 4 Aim: b) Using a deep feed forward network with two hidden layers for performing classification and predicting the probability of class.
Practical 4 Aim: c) Using a deep feed forward network with two hidden layers for performing linear regression and predicting values.
Practical 5 Aim: a) Evaluating feed forward deep network for regression using KFold cross validation.
Practical 5 Aim: b) Evaluating feed forward deep network for multiclass Classification using KFold cross-validation.
Practical 6 Aim: Implementing regularization to avoid overfitting in binary classification.
Practical 7 Aim: Demonstrate recurrent neural network that learns to perform sequence analysis for stock price.
Practical 8 Aim: Performing encoding and decoding of images using deep autoencoder.
Practical 9 Aim: Implementation of convolutional neural network to predict numbers from number images
Practical 10 Aim: Denoising of images using autoencoder
Question Bank
Unit 1 Question Bank
Unit 2 Question Bank
Unit 3 Question Bank
Unit 4 Question Bank
Unit 5 Question Bank