Fundamentals of Deep Learning
About the training
Machine learning comprises those methods used in AI for building knowledge on the basis of past experience. The algorithms used in the field analyze training data in order to identify patterns and laws. These are then put into use to evaluate new situations as part of a general diagnosis of data, in fields including voice, text and image recognition as well as autonomous systems (e.g. robots). Deep learning is a specialized method for machine learning whose purpose is to train complex artificial neural networks. In recent years, this method has made it possible for highly promising successes to be achieved in the field of AI.
Goal
You’ll become acquainted with fundamental methods of machine learning as well as their possible applications. You’ll understand the areas of application of neural networks, in particular those of deep learning, and have the ability to assess their possibilities as well as limitations. You’ll be able to assess the technologies we cover in the course and determine whether you can apply them profitably in your business and for key tasks.
Target group
Technical decision makers, innovation project managers, software architects
Content
Foundations
- What is machine learning?
- The difference between machine learning, artificial intelligence and data science
Introduction to the methodological foundations
- Difference to traditional software development
- Components of machine learning (mathematical & statistical models, training data)
Prerequisites for machine learning
- Requirements for data
- Predictability requirements
Learning approaches for machine learning
- Supervised
- Unsupervised
- Reinforcement
Different models of machine learning
- Decision tree
- Neural Networks
- Regression analysis
Deep learning
- What is an artificial neural network?
- What is deep learning?
- Advantages of deep learning
General applications of machine learning
- Predictions
- Probabilities for events
- Process optimization
- Adaptable systems
Examples of technical applications
- Recommendation systems
- Image recognition
- voice recognition
- automation
Areas of application in companies
- Finance (e.g. fraud detection)
- Marketing
- Healthcare
- Customer support
Machine learning platforms & technologies
- Google TensorFlow
- IBM
- Microsoft Azure
- Amazon AWS
- Open source platforms
- ML programming languages
Are you looking for more?
We offer you this course as an inhouse training for your company. Here you can learn more about our service packages. Tell us more about your needs, so that we can tailor an offer that perfectly fits your requirements.
We offer this course also on several dates as a webinar and face-to-face training in German. You can find all upcoming dates for our open trainings in German on our Website.