Deep Learning: Model Optimization and Tuning

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Kumaran Ponnambalam

54:24

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1. Introduction

001. Optimizing neural networks.mp4

00:49

002. Prerequisites for the course.mp4

02:16

003. Setting up exercise files.mp4

02:21

2. Introduction to Deep Learning Optimization

004. What is deep learning.mp4

01:40

005. Review of artificial neural networks.mp4

02:31

006. An ANN model.mp4

01:34

007. Model optimization and tuning.mp4

01:30

008. The deep learning tuning process.mp4

02:54

009. Experiment setups for the course.mp4

02:03

3. Tuning the Deep Learning Network

010. Epoch and batch size tuning.mp4

01:57

011. Epoch and batch size experiment.mp4

03:01

012. Hidden layers tuning.mp4

01:57

013. Determining nodes in a layer.mp4

02:00

014. Choosing activation functions.mp4

01:58

015. Initializing weights.mp4

01:43

4. Tuning Back Propagation

016. Vanishing and exploding gradients.mp4

02:23

017. Batch normalization.mp4

01:49

018. Optimizers.mp4

01:25

019. Optimizer experiment.mp4

01:13

020. Learning rate.mp4

01:11

021. Learning rate experiment.mp4

01:13

5. Overfitting Management

022. Overfitting in ANNs.mp4

01:49

023. Regularization.mp4

00:51

024. Regularization experiment.mp4

00:51

025. Dropouts.mp4

00:59

026. Dropout experiment.mp4

00:57

6. Model Tuning Exercise

027. Tuning exercise Problem statement.mp4

03:55

028. Acquire and process data.mp4

00:51

029. Tuning the network.mp4

01:02

030. Tuning backpropagation.mp4

00:50

031. Avoiding overfitting.mp4

00:50

032. Building the final model.mp4

01:21

7. Conclusion

033. Continuing your deep learning journey.mp4

00:40

Description

Deep Learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use Deep Learning as their foundational technology. Studying this technology, however, presents several challenges. IT professionals from varying backgrounds need a simplified resource to learn the concepts and build models quickly. In this course, instructor Kumaran Ponnambalam provides a simplified path to understand various optimization and tuning options available for deep learning models and shows you how to use these options to improve models. He begins by reviewing Deep Learning, including artificial neural networks and architectures. Next, Kumaran discusses the process of hyper parameter tuning. He examines the building blocks of neural networks and the levers available to tune them. Kumaran offers recommendations and best practices. Then he concludes with an end-to-end tuning example.

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Deep Learning

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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