Prerequisites: Familiarity with fundamental deep learning concepts (neural networks, activation functions, loss functions, backpropagation), Python programming, and experience with a deep learning framework (e.g., TensorFlow or PyTorch).

Level: Intermediate

What You'll Learn

• 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.