Deep Learning: Model Optimization and Tuning
Offered By: LinkedIn Learning
Course Description
Overview
Learn about various optimization and tuning options available for deep learning models and use them to improve models.
Syllabus
Introduction
- Optimizing neural networks
- Prerequisites for the course
- Setting up exercise files
- What is deep learning?
- Review of artificial neural networks
- An ANN model
- Model optimization and tuning
- The deep learning tuning process
- Experiment setups for the course
- Epoch and batch size tuning
- Epoch and batch size experiment
- Hidden layers tuning
- Determining nodes in a layer
- Choosing activation functions
- Initializing weights
- Vanishing and exploding gradients
- Batch normalization
- Optimizers
- Optimizer experiment
- Learning rate
- Learning rate experiment
- Overfitting in ANNs
- Regularization
- Regularization experiment
- Dropouts
- Dropout experiment
- Tuning exercise: Problem statement
- Acquire and process data
- Tuning the network
- Tuning backpropagation
- Avoiding overfitting
- Building the final model
- Continuing your deep learning journey
Taught by
Kumaran Ponnambalam
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