Learn Artificial Intelligence and Deep Learning today to unlock the Jobs of tomorrow
How it works
Step by step
- Deep Learning and AI in general are not easy.
- That's why it is important to understand everything in detail, one topic after an other.
- As you'll advance through the content, you'll find yourself able to solve more complex problems.
Project-Based
- Theory will not remain just theory.
- Along the way you will test your newly gained knowledge on a lot of practical exercises.
- At the end of the program, you can work on several capstone projects and extend your Deep Learning portfolio.
One-on-One Mentoring
- "Just learning" is oftentimes not enough. Rather you require help from someone who is more experienced than you.
- For this reason during the extercices and the projects you will get personal support and mentoring.
Who we are
This is an advanced Deep Learning education and mentoring platform that provides practical state-of-the-art Deep Learning teaching for professionals and beginners. Our goal is to prepare you to work proffesionaly as a Deep Learning Engineer. For this we offer course-based learning that is complemeted by one-on-one mentoring and support of the students.
The main focus of teaching lies in very practical in-depth-learning and working on real Deep Learning Projects using real-world datasets. Among many other things you are going to learn how to implement neural network models from scratch in TensorFlow and Python to solve real business problems and deploy then models as micro-service applications.
What Our Students Say
Great, well structured course, ideal for those who want to work on real deep learning projects. Exercises and code examples are also very helpful to solidify the knowledge. I recommend this course to anyone who wants to work in the field of Deep Learning professionally.
Johannes Lueken, CEO MiiMedia
This course goes beyond neural networks. Instead, it covers topics that are crucial for a professional career in Deep Learning. Just to name a few: Best practices for implementing neural network models, data-preprocessing, efficiency, GPU-Training, model evaluation, deployment of the models to production etc.
Vitali Rudi, Industrial Engineer
The best thing for me personally was to see the different steps of a typical deep learning project and how to approach these steps in practice. I'm now more confident about taking part in a project myself and solving problems with neural networks.
Adile Kilic, Economical Engineer
