This series takes engineers from zero to expert in machine learning in 5 sessions. The world needs more people who understand machine learning, and our goal is to get you started on that path as efficiently as possible. While there are plenty of online resources, we know it's tough to learn a technical topic without a teacher. This workshop will arm you with the tools to get started using machine learning in your day job, and the resources to find additional help if you want to go deeper.

Part 1 - Introduction to Deep Learning
Neural networks are powerful beasts that give you a lot of levers to tweak! The sheer size of customizations that they offer can be overwhelming to even seasoned practitioners. In this talk, Lavanya will give you a framework for making smart decisions about your neural network architecture!

We’ll explore lots of different facets of neural networks in this talk, including how to setup a basic neural network (including choosing the number of hidden layers, hidden neurons, batch sizes etc.) We’ll learn about the role momentum and learning rates play in influencing model performance. And finally we’ll explore the problem of vanishing gradients and how to tackle it using non-saturating activation functions, BatchNorm, better weight initialization techniques and early stopping.

Deep Learning Frameworks
1. Numpy
2. Pandas
3. TensorFlow
4. Keras

Deep Learning Architectures
1. Perception
2. MLPs
3. CNNs

Applications
1. Building convolutional neural networks
2. Image Recognition

Prerequisites
This class is designed for working engineers with no experience in machine learning.  Some students have taken this class after taking an online machine learning course and have enjoyed the practical applications and review.  The entire series will be taught in Python. If you are not familiar with Python, be extra sure to have everything installed in advance and consider doing a quick online tutorial.

Deep Learning Master Class I - Introduction