Deep learning Part 2 Vision Algorithms
Deep Learning is the biggest change happening in computer science right now. It powers everything from Google’s Alpha Go to Tesla’s autopilot to Amazon’s Echo. Every company is trying to figure what it’s AI strategy is going to be. Deep learning makes all kinds of new applications possible but presents a whole new set of challenges like exotic hardware and non-determinism. That’s why companies can’t hire experts fast enough. We strongly believe you don’t need a degree from Stanford or MIT to build your own algorithms and use this amazing technology.
While there are plenty of online resources, we know it's tough to learn a technical topic without a teacher. We're bringing together expert engineers in the field of machine learning, deep learning & AI who will help you learn the basics in a hands-on approach to learning deep learning.
This is a hands-on course to take students from little knowledge of deep learning to comfort building real world models. It requires very little math, but reasonably proficient programming skills. It’s designed as a follow up to Introduction to AI, Machine learning & Deep Learning, but might also be appropriate for someone who took the coursera course in machine learning and wanted something very hands on.
Deep Learning Frameworks
Deep Learning Model Architectures
Convolutional Neural Network\
1. Completion of our introductory course “Technical Introduction to AI, Machine Learning & Deep Learning”
2. Engineers with some machine learning experience but not necessarily neural nets. Students should be pretty familiar with concepts like "training data" and "cross validation". Students should be familiar with python, some familiarity with the numpy library is helpful.
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
-Practical high-level knowledge of how deep learning algorithms actually work
-Familiarity with popular frameworks, TensorFlow, Caffe, Torch, especially Keras
-How to install the frameworks so they take advantage of your GPUs
-How to build models from scratch
-How to fine tune popular models like Inception and ResNet when training data is limited
Morning: Introduction to Neural Nets
9:00 – 10:00 Breakfast and Laptop Setup
10:00 - 11:00 Refresher on machine learning, High-level overview of deep learning with optional calculus.
11:00- 12:00 Build a small neural network from scratch together in python to do digit recognition
1:00-2:00 Build a convolutional neural net object recognizer together in keras/tensorflow from scratch. Take existing CNNs and build more recognizers.
2:00-3:00 Build a bounding box object recognizer together in keras/tensorflow.
3:00-4:00 Build a semantic segmentation system together in keras/tensorflow.
4:00-5:00 How to apply cnns to more exotic problems. How to deploy and debug cnns in te real world.
Testimonials and Feedback
"I found it to be really engaging and interesting. I was already familiar with some ML concepts, so it helped me understand them better and think about how to apply them. The code samples are really great and will definitely reference them in the future. I thought the class went at a generally good pace."
"Good experience - full of great resources and discussion. Good, practical intro for new folks, and also valuable for those familiar with the basics. I walked away excited to experiment!"
“Class was great, you ticked off my curiosity. I am excited to review the content and retry it by myself. Thank you for encouraging peer to peer collaboration and making the effort to build the slack channel. I think it was nice to see you debug live.”
We also do corporate trainings - send us an email for details.