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Practical 1

Aim: Performing matrix multiplication and finding eigen vectors and eigen values using TensorFlow

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Practical
- Practical 1 Aim: Performing matrix multiplication and finding eigen vectors and eigen values using TensorFlow
- 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

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