Overview
Discover 30 essential guidelines for optimizing deep learning models, enhancing performance, and achieving better results in AI and machine learning projects.
Syllabus
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- Introduction to Deep Learning Performance
-- Overview of deep learning and its performance challenges
-- Importance of optimization in AI projects
- Data Handling and Preprocessing
-- Rule 1: Data Normalization Techniques
-- Rule 2: Effective Data Augmentation
-- Rule 3: Handling Imbalanced Datasets
- Model Architecture
-- Rule 4: Choosing the Right Model Size
-- Rule 5: Exploring Different Layer Types
-- Rule 6: Balancing Depth and Width in Networks
- Training Strategies
-- Rule 7: Selecting Suitable Learning Rates
-- Rule 8: Using Learning Rate Schedulers
-- Rule 9: Employing Early Stopping
- Regularization Techniques
-- Rule 10: Understanding Dropout
-- Rule 11: Implementation of L1 and L2 Regularization
-- Rule 12: Batch Normalization Benefits
- Optimization Algorithms
-- Rule 13: Overview of Optimization Algorithms
-- Rule 14: Momentum and Nesterov Accelerated Gradient
-- Rule 15: Adapting to Adam and its Variants
- Evaluation and Metrics
-- Rule 16: Choosing the Right Evaluation Metrics
-- Rule 17: Avoiding Overfitting through Cross-Validation
- Hyperparameter Tuning
-- Rule 18: Methods for Hyperparameter Tuning
-- Rule 19: Grid Search vs. Random Search
-- Rule 20: Bayesian Optimization
- Computational Efficiency
-- Rule 21: Efficient Use of GPUs and TPUs
-- Rule 22: Mixed Precision Training
-- Rule 23: Model Pruning and Quantization
- Handling Large Datasets
-- Rule 24: Strategies for Distributed Training
-- Rule 25: Data Parallelism vs. Model Parallelism
- Transfer Learning and Fine-Tuning
-- Rule 26: Leveraging Pre-trained Models
-- Rule 27: Fine-Tuning Strategies for Specific Tasks
- Monitoring and Debugging
-- Rule 28: Tools for Monitoring Training
-- Rule 29: Debugging Techniques for Deep Learning Models
- Model Deployment
-- Rule 30: Preparing Models for Production Environments
- Conclusion and Future Trends
-- Summary of 30 Golden Rules
-- Emerging Trends in Deep Learning Performance
- Supplemental Resources
-- Recommended Books and Articles
-- Online Tools and Libraries
Taught by
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On-Demand
