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LinkedIn Learning

Deep Learning: Model Optimization and Tuning

via LinkedIn Learning

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
1. Introduction to Deep Learning Optimization
  • 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
2. Tuning the Deep Learning Network
  • Epoch and batch size tuning
  • Epoch and batch size experiment
  • Hidden layers tuning
  • Determining nodes in a layer
  • Choosing activation functions
  • Initializing weights
3. Tuning Back Propagation
  • Vanishing and exploding gradients
  • Batch normalization
  • Optimizers
  • Optimizer experiment
  • Learning rate
  • Learning rate experiment
4. Overfitting Management
  • Overfitting in ANNs
  • Regularization
  • Regularization experiment
  • Dropouts
  • Dropout experiment
5. Model Tuning Exercise
  • Tuning exercise: Problem statement
  • Acquire and process data
  • Tuning the network
  • Tuning backpropagation
  • Avoiding overfitting
  • Building the final model
Conclusion
  • Continuing your deep learning journey

Taught by

Kumaran Ponnambalam

Reviews

4.6 rating at LinkedIn Learning based on 208 ratings

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