This workshop covers the fundamental components of Machine Learning, Deep Learning, the Python ecosystem and Tensorflow. You will understand what it means, how it works, and how to build various neural networks such as Fully Connected Neural Networks, Deep Convolutional Networks, and Recurrent Neural Networks.
A major focus of this workshop will be to not only understand how to build the necessary components of state of the art machine learning models, but also how to apply them for exploring solution-oriented applications. Combined with several advanced topics of Deep Learning and AI, participants will learn the necessary engineering tricks for making neural networks work on practical problems.
Machine Learning Fundamentals
- Development Environment
- The Python Ecosystem
- Training in the Cloud
Understanding Neural Networks
- Deep Learning architectures and their applications
- Fully Connected Neural Networks: Predictive Modeling
- Convolutional Neural Networks: Image Recognition
- Recurrent Neural Networks: Language Processing
Tensorflow for Hackers
- Live-coding session
- Neural Network Training in Practice
Advanced Topics of Deep Learning and AI (Overview)
- Transfer Learning
- Unsupervised Learning
- Reinforcement Learning
- Generative Models: GAN
Who should apply?
Developers, Data Scientists, Data Engineers.
Participants should have experience with a programming language, ideally with Python.
Familiarity with basic math concepts such as linear algebra (vector and matrix calculus) and probability theory is advantageous.
Price per workshop participant, group discounts upon request
Maximum number of participants: 10
Language: English or German
Duration: 1 day
After you booked the course, you will be provided with a link for choosing your preferred date for the workshop.