To teach mathematical fundamentals about deep learning, to use open source libraries related to deep learning, to develop deep learning applications.

Introduction to Artificial incelligence, machine learning and deep learning. Mathematical background. Gradient Descent algorithms, loss functions, backpropogation. Keras deep learning library,  tensor operations in Python.  Multi label classification, Regression. Data prepocessig, overfitting, weight regularization, dropout. 2D Convolution (conv2D) neural networks (convnets), pooling. Image data augmentation. Pretrained networks, fine tuning Visualizing convnet filters. Deep learning with text data. Emedding layers. Recurrent neural networks, LSTM and GRU layers. 1D convnets for sequence data.  Keras functional API,  Multi input multi output models. Generative deep learning.  Presentations about state of the art topics.