Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization Course Overview
This course provides essential techniques for enhancing deep neural networks through effective hyperparameter tuning, regularization, and optimization strategies. Tailored for data scientists, machine learning engineers, and AI researchers, it equips professionals with the skills to improve model performance, reduce overfitting, and optimize computational efficiency, ultimately driving better outcomes in various AI applications.
Course outline & what you'll learn
Overview of deep neural networks
- Importance of hyperparameter tuning, regularization, and optimization
- Definition and significance of hyperparameters
- Common hyperparameters in deep learning
- Grid search
- Random search
- Bayesian optimization
- Best practices for hyperparameter selection
- Understanding overfitting and underfitting
- L1 and L2 regularization
- Dropout
- Batch normalization
- Data augmentation
- Implementing regularization in neural networks
Overview of optimization in neural networks
- Gradient descent and its variants
- Stochastic gradient descent (SGD)
- Momentum
- Nesterov accelerated gradient
- Adam optimizer
- RMSprop
- AdaGrad
- Learning rate schedules and adjustments
- Metrics for evaluating model performance
- Cross-validation techniques
- Choosing the right model based on evaluation results
- Tools and frameworks for deep learning
- Hands-on projects for applying learned techniques
- Case studies of successful hyperparameter tuning, regularization, and optimization
- Recap of key concepts
- Emerging trends in deep learning optimization
- Resources for further learning and exploration
Why train with Traincrest
This Open Source course is delivered by Traincrest's certified instructors, live online or in the classroom, with hands-on labs and a 98% exam success rate. Trusted by 500+ companies and 50,000+ students worldwide.