Fundamentals of Deep Learning
Do you like to think analytically, are you enthusiastic about the fact that machines can learn and would you like to get to know the potential of neural networks and especially deep learning? If you would like to find out everything about current developments in the field of deep learning, then look forward to this training. Together we train systems that learn tasks in order to then carry them out automatically.
Certificate of attendance from Spirit in Projects Foundation
Goals
- Understand machine learning methods
- Get to know the structure and functionality of neural networks
- Apply deep learning to train neural networks
- Understand and use application areas such as pattern recognition, image and text processing and object recognition
- Use tools and platforms for deep learning
Target Groups
Content
1. Basics
- What is Machine Learning?
- The difference between machine learning and artificial intelligence and data science
2. Introduction to the methodological basics
- Difference from traditional software development
- Components of machine learning
3. Requirements for machine learning
- Data requirements
- Requirements for predictability
4. Learning approaches for machine learning
- Supervised
- Unsupervised
- Reinforcement
5. Machine learning model types
- Decision Tree
- Neural Networks
- Regression analysis
6. Deep learning
- What is an artificial neural network?
- What is Deep Learning?
- Benefits of Deep Learning
- Simple deep neural networks
- Convolutional Neural Networks
- GAN
- Transformers
7. ML platforms and technologies
- Google TensorFlow
- PyTorch
- Repositories
- ML programming languages
Certification
For this training you will receive a certificate of participation from Spirit in Projects.
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