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EVENT DETAILS |
You will learn
This series takes engineers from zero to expert in machine learning in 5 sessions. The world needs more people who understand machine learning, & 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, & the resources to find additional help if you want to go deeper.
Part 2 - Introduction to Computer Vision
This class is part two of our series that takes engineers from zero to one in deep learning. Its 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 & wanted to really focus on deep learning algorithms & computer vision.
We will look at the following frameworks & architectures
Deep Learning Frameworks
1. Numpy
2. TensorFlow
3. Keras
Deep Learning Model Architectures
1. Autoencoders
2. Convolutional Neural Networks
3. Adversarial Networks
4. Perceptron
5. MLPs
We will discuss some of the more popular application of computer vision including
1. Object detection
2. Image segmentation
3. Bounding Boxes
4. Building convolutional neural networks
5. Image Recognition
Hosts
Lavanya, Machine Learning engineer (https://twitter.com/lavanyaai)
Upka Lidder, IBM Developer Advocate (https://twitter.com/lidderupk)
You can watch the previous sessions
1. Deep Learning Master Class I - Introduction:
https://www.crowdcast.io/e/deep-learning-master
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 & have enjoyed the practical applications & 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 & consider doing a quick online tutorial.
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