Learn practical techniques to prevent overfitting and improve the training speed and performance of deep learning models. This course covers common regularization methods like L1/L2, Dropout, and Batch Normalization, alongside standard optimization algorithms such as SGD, Momentum, RMSprop, and Adam. Get hands-on experience implementing these techniques using popular deep learning frameworks.
Prerequisites Python & DL Basics
Level:
Overfitting Diagnosis
Identify overfitting and underfitting in deep learning models using learning curves and validation metrics.
Regularization Methods
Understand and implement L1, L2, Elastic Net regularization techniques.
Dropout Application
Apply Dropout effectively to reduce overfitting in neural networks.
Batch Normalization
Implement Batch Normalization to stabilize and accelerate network training.
Optimization Algorithms
Differentiate between various gradient descent optimization algorithms (SGD, Momentum, RMSprop, Adam) and understand their mechanics.
Hyperparameter Tuning
Understand the interaction between optimizers, learning rates, and regularization hyperparameters.
Practical Implementation
Implement regularization and optimization techniques within a standard deep learning workflow using a common framework.
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