This class is part two of our series that takes engineers from zero to one in deep learning.

It’s designed as a follow up to Technical Introduction to AI, Machine Learning & Deep Learning but could also be appropriate for someone who had done some machine learning and wanted to really focus on deep learning algorithms and computer vision.

This is a hands-on course that requires very little math, but reasonably proficient programming skills. At the end of class students will be able to build, inspect debug and deploy deep learning for a variety of applications in vision.

Technologies Introduced and Used:

• Deep Learning/Data Science Frameworks

• NumPy

• Keras

• TensorFlow

• Deep Learning Model Architectures

• Multi-Layer Perceptron

• Convolutional Neural Networks

• Adversarial Networks

• Applications

• Object detection

• Image segmentation

• Bounding Boxes

Prerequisites:

This is a sequel to our first-course Introduction to AI, Machine learning & Deep Learning. It is a series designed for practicing engineers who want to get into deep learning.

You can also skip the first course if you have experience with machine learning and feel like you know most of what is covered in part one.

The entire course is done in python, so if you are unfamiliar with python you should brush up. No math is required but we will use a little bit of calculus. It’s ok if you don’t follow along in those parts.

What you need to bring:

Students need to bring a laptop. We have detailed setup instructions at https://github.com/lukas/ml-class/blob/master/README.md

If you want to run on a GPU either on your laptop or in the cloud for this course, you can. We have setup instructions for an AWS machine at https://github.com/lukas/ml-class/blob/master/aws.md

You'll learn:

- Practical high-level knowledge of how deep learning algorithms actually work
- How to install the frameworks so they take advantage of your GPUs
- How to build models from scratch
- How to debug models when they don’t work
- How to fine tune popular models like Inception and ResNet when training data is limited
- How to deploy models effectively

Instructors:

Lukas Biewald is the founder of CrowdFlower, an Artificial Intelligence company that works with data science teams at Google, Bloomberg, Facebook and hundreds of other organizations to make machine learning work in the real world.

Prior to that, Lukas was the first data scientist at Powerset (Acquired by Microsoft and rebranded as Bing) and a scientist at Yahoo!, Lukas was shipping machine learning algorithms to hundreds of millions of users.

Lukas frequently teaches invited Machine Learning workshops with Galvanize, O’Reilly and ODSC. He is a contributor to Computerworld, Forbes and O’Reilly and has presented at the machine learning academic conferences such as AAAI, SIGIR, ACL and EMNLP. He was in Inc’s annual 30 under 30 and was also a finalist at TechCrunch Disrupt.

Robert Munro is the VP of Machine Learning at CrowdFlower and an expert in combining Human and Machine Intelligence, working with Machine Learning approaches to text, speech, image and video processing. Robert has founded several AI companies, building some of the top teams in Artificial Intelligence. He has worked in many diverse environments, from Sierra Leone, Haiti and the Amazon, to London, Sydney and Silicon Valley, in organizations ranging from startups to the United Nations. He most recently ran Product for AWS’s first Natural Language Processing services in the Deep Learning team at Amazon AI.

Robert has published more than 50 papers and is a regular speaker about technology in an increasingly connected world. He has a PhD from Stanford University.

Curriculum:

Morning: Introduction to Neural Nets

9:00 – 10:00

Overview of deep learning history and terminology and why it matters. Loss functions, backpropagation and more.

10:00 - 12:00

Overview of keras, tensorflow and numpy and how they are all different. Build a small neural network from scratch together in python to do digit recognition.

12:00-1:00

Lunch

Afternoon: Vision

1:00 - 2:00

Transfer learning and fine-tuning. Build a dog vs cat classifier in several different ways.

2:00 - 3:00

Build and deploy and improve a facial emotion classifier.

4:00 - 5:00

How to apply the work to semantic segmentation and bounding box detection. Adversarial learning and the future of vision classifiers.

5:00-7:00 Drinks & Networking

We’ll bring together top entrepreneurs, tech executives & engineers to connect with and learn from. Plus, this is a chance to meet your classmates and teachers in an informal and fun setting.