Fundamentals of Deep Learning Lab

 List of Experiments:

1. To explore the basic features of Tensorflow and Keras packages in Python
2. Implementation of ANN model for regression and classification problem in Python.
3. Implementation of Convolution Neural Network for MRI Data Set in Python.
4. Implementation of Autoencoders for dimensionality reduction in Python.
5. Application of Autoencoders on Image Dataset.
6. Improving Autocoder’s Performance using convolution layers in Python (MNIST Dataset to be utilized).
7. Implementation of RNN model for Stock Price Prediction in Python
8. Using LSTM for prediction of future weather of cities in Python
9. Implementation of transfer learning using the pre-trained model (MobileNet V2) for image classification in Python.
10. Implementation of transfer learning using the pre-trained model (VGG16) on image dataset in Python.
11. NLP Analysis of Restaurant Reviews in Python.
12. Building a NLP model for Spam Detection using TFIDF (Term Frequency Inverse Document Frequency Vectorizer).