Deep Learning with Python: Optimizing Deep Learning Models
2h 1mIntermediate2025-02-24
Authors
Frederick Nwanganga
Information Technology Professional and Teacher
Course details
Discover techniques to optimize deep learning models by improving their performance and efficiency. Emphasizing practical applications, instructor Frederick Nwanganga guides you through hands-on coding exercises, covering the essentials of data preprocessing and augmentation, regularization methods to minimize overfitting, optimization algorithms, advanced hyperparameter tuning methods, and more.
Learning objectives
Understand the various techniques used to optimize deep learning models.
Preprocess and augment both text and image data.
Leverage Python to implement regularization techniques in a deep learning model to minimize overfitting.
Understand how different optimization algorithms work and when best to use them.
Use Python to tune the hyperparameters of a deep learning model.
Learning objectives
Understand the various techniques used to optimize deep learning models.
Preprocess and augment both text and image data.
Leverage Python to implement regularization techniques in a deep learning model to minimize overfitting.
Understand how different optimization algorithms work and when best to use them.
Use Python to tune the hyperparameters of a deep learning model.
Skills covered
Neural Networks and Deep LearningPythonArtificial Intelligence (AI)Programming LanguagesOpen SourceSoftware DevelopmentOne-Off
Concepts
Introduction
- Optimizing deep learning models
- What you should know
- Using the exercise files
Optimizing Deep Learning Models
- The importance of optimizing deep learning models
Regularization Techniques
- The bias-variance trade-off
- Lasso and ridge regularization
- Applying L1 regularization to a deep learning model
- Applying L2 regularization to a deep learning model
- Elastic Net regularization
- Dropout regularization
- Applying dropout regularization to a deep learning model
Loss Functions and Optimization Algorithms
- Common loss functions in deep learning
- Batch gradient descent
- Stochastic gradient descent (SGD)
- Mini-batch gradient descent
- Adaptive Gradient Algorithm (AdaGrad)
- Root Mean Square Propagation (RMSProp)
- Adaptive Delta (AdaDelta)
- Adaptive Moment Estimation (Adam)
Hyperparameter Tuning Techniques
- Parameters versus hyperparameters
- Key hyperparameters in deep learning
- Methods for hyperparameter tuning
- Defining a tunable deep learning model in Keras
- Using KerasTuner for hyperparameter tuning
Advanced Training Techniques
- Batch normalization
- Applying batch normalization to a deep learning model
- Gradient clipping
- Applying gradient clipping to a deep learning model
- Early stopping and checkpointing
- Learning rate scheduling
- Training a deep learning model using callbacks
Conclusion
- Continuing to optimize deep learning models