With the resurgence of Neural Networks in the 2010s, Deep Learning has become essential for Machine Learning practitioners and even many software engineers.

Here I do provide a comprehensive introduction of mathematics for Data Scientists and software engineers with Machine Learning experience.

I will start with Deep Learning basics, explain the concepts, with visual explanation through Python code, and move quickly to the details of important advanced architectures, how these basics used in implementing Deep Neural Networks everything from scratch along the way.

I will demonstrate how Neural Networks work in a principled approach.

This will cover:
1. Neural Networks basics and Forward propagation
2. The importance of Back propagation
3. Optimisation Algorithms
4. Perform train/test splits and examine production case studies
5. Deep Neural network explanation : Conceptually + Visually + Mathematically with the help of python code