Major Course Contents:
1. Introduction to Artificial Intelligence, Machine Learning, Deep Learning and Representation Learning
2. Machine Learning with Feature Representation
3. Machine Learning Basics – Linear Regression, Perceptron’s,
Multilayer Perceptron’s, Stochastic Gradient Descent,
Backpropagation
4. Deep Learning Regularization Techniques: L1, L2, Noise
Injection, Data Augmentation, Dropout, Ensemble,
Parameter Sharing, etc.
5. Basics of TensorFlow/Keras/PyTorch/Python
6. CNN Architecture and Advanced Models
7. Object detection RCN and FRCNN
8. Recommendation Systems, BERT
9. Computer Vision Applications: Image Classification, Object
Recognition and detection using TensorFlow
10.RNN Architecture and LSTM, Google Colab
11.Natural Language Processing(NLP) Applications : Machine
Translation using TensorFlow/PyTorch
12.Current Research – Latest Architectures, Applications
https://nitw.ac.in/eict/files/EICT_DLAI_GNITS.pdf