| no | Chapters completed | description |
|---|---|---|
| 1 | Introducing deep learning | |
| 2 | Fundamental concepts | Types of machine learning : Supervised/Unsupervised, parametric/nonparametric |
| 3 | Introduction to neural prediction | Implementation of simple neural network using python. Making single/multiple predictions using single/muliple inputs. Element/ Vector/ matrix multiplications. Numpy basics. |
| 4 | Introduction to neural learning: gradient descent | Hot and cold learning. Gradient Descent. Overshoot. Divergence. |