Training and Optimization
Master of Science in Computer Science · University · Artificial Intelligence
This unit delves into the fundamental concepts of training and optimizing deep learning models. Students will explore various algorithms and techniques to enhance model performance and accuracy, gaining a comprehensive understanding of the underlying principles and best practices.
Zum Üben gibt es 56 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Supervised Learning and Deep Learning Architectures
- Gradient Descent and Backpropagation
- Optimization Algorithms (e.g.
- SGD
- Adam
- RMSProp)
- Regularization Techniques (e.g.
- L1
- L2
- Dropout)
- Hyperparameter Tuning
- Model Evaluation Metrics
Stichworte
- Gradient Descent
- Backpropagation
- Optimization Algorithms
- Regularization
- Model Evaluation
- Deep Learning Architectures