We host a practical deep learning course, in two flavors, for C-level and management as well as developers, covering just enough theory to get you going and focus on ways to be productive.
We cover deep learning in four topics:
Have a look at our coming sessions: link
A bit more detail
Artificial intelligence and deep learning are taking the world by storm, allowing simple creation of things that were previously digitally impossible. No sector appears to be safe.
But does the hype exceed reality? Does this mean we will all be programming neural networks? Or will every business be replaced by one of the big five tech companies or a fresh startup?
In this course, we keep our heads in the clouds and our feet on the ground. With a practical rather than theoretical approach, we will cover the ins and outs of the current state of the art. What is deep learning good at? Where is it more research than reality? Hoe do I make my company smarter, and do I need to be a math genius with lots of computers and data?
We will cover just enough theory to get you started and focus on ways to be productive.
The course comes in a business and managent and developer flavor and covers deep learning in four topics:
Starting from simple definitions, we define what artificial intelligence, machine learning, and deep learning are precisely, and what they are (not) good at, and why this is working now. We explore examples of computer vision, natural language processing and some special cases. Finally, we debunk some well-spread AI myths.
Neural networks are at the heart of the current AI and DL success. We will explain what they are exactly and how they work, without fear of getting our hands a little dirty and discuss the different ways computers can learn. We will dive into specific cases of computer vision and NLP, and how that works, where their strenghts lie and what it is they exactly learn. We will have a look at different frameworks, how they can benefit you, and introduce transfer learning as a fundamental building block, creating world-class networks as we go.
The cutting and bleeding edge of AI is moving fast, with new techniques, possibilities and better performing networks being released. We discover what is coming adn discuss how and where that might hurt when applying AI. We also discuss “ethics and AI” and new datasets.
While a lot of the AI focus is on research and publications, we also look at the bigger picture. In this final topic, we go through the different steps of an AI project, looking at the value proposition, data collection, preparation, and clearning, “build vs buy”, where we look at what is being offered by saas providers, and putting your trained model into production, both in the cloud as on mobile.