Deep Learning Building Blocks
Training Provider: SINGAPORE MANAGEMENT UNIVERSITY
Course Reference: TGS-2024048993
S$660
Original: S$2,200
Save S$1,540
About This Course
This module builds on the knowledge covered in the previous module to provide participants with foundational knowledge essential for constructing effective deep learning models. Divided into four sessions, participants will explore optimization algorithms, activation functions, regularization techniques, and initialization strategies, crucial for understanding and implementing deep learning algorithms.
What You'll Learn
- Describe optimization algorithms' principles for efficient model training.
- Apply optimization algorithms to training processes to improve model convergence and performance.
- Apply activation functions appropriately in neural network architectures, considering their effects on model training and convergence.
- Implement and analyze regularization techniques such as L1 and L2 regularization and dropout to mitigate overfitting and enhance model generalization and robustness.
- Apply initialization strategies to deep learning architectures, ensuring stable and efficient model training.
- Apply optimization algorithms to training processes to improve model convergence and performance.
- Apply activation functions appropriately in neural network architectures, considering their effects on model training and convergence.
- Implement and analyze regularization techniques such as L1 and L2 regularization and dropout to mitigate overfitting and enhance model generalization and robustness.
- Apply initialization strategies to deep learning architectures, ensuring stable and efficient model training.
Entry Requirements
Nil
Course Details
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Note: To apply for this course, visit the SkillsFuture website or contact the training provider directly.
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