Deep Learning: Model Optimization and Tuning

Offered By: LinkedIn Learning

Tags

Deep Learning Courses Neural Networks Courses Gradient Descent Courses Overfitting Courses Model Optimization Courses Regularization Courses Hyperparameter Tuning Courses Batch Normalization Courses Activation Functions Courses Backpropagation Courses

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

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

Related Courses

Neural Networks for Machine Learning
University of Toronto via Coursera Good Brain, Bad Brain: Basics
University of Birmingham via FutureLearn Statistical Learning with R
Stanford University via edX Machine Learning 1—Supervised Learning
Brown University via Udacity Fundamentals of Neuroscience, Part 2: Neurons and Networks
Harvard University via edX