Performance Tuning Deep Learning in Python - A Masterclass

person icon Packt Publishing

Performance Tuning Deep Learning in Python - A Masterclass

This is a step-by-step course in getting the most out of deep learning models on your own predictive modeling projects.

updated on icon Updated on Sep, 2023

language icon Language - English

person icon Packt Publishing

architecture icon Development,Data Science and AI ML,Deep Learning,Python


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Course Description

Deep learning neural networks have become easy to create. However, tuning these models for maximum performance remains something of a challenge for most modelers. This course will teach you how to get results as a machine learning practitioner.

The course starts with an introduction to the problem of overfitting and a tour of regularization techniques. Learn through better configured stochastic gradient descent batch size, loss functions, learning rates, and to avoid exploding gradients via gradient clipping. After that, you’ll learn regularization techniques and reduce overfitting by updating the loss function using techniques such as weight regularization, weight constraints, and activation regularization. Post that, you’ll effectively apply dropout, the addition of noise, and early stopping, and combine the predictions from multiple models.

You’ll also look at ensemble learning techniques and diagnose poor model training and problems such as premature convergence and accelerate the model training process. Then, you’ll combine the predictions from multiple models saved during a single training run with techniques such as horizontal ensembles and snapshot ensembles.

Finally, you’ll diagnose high variance in a final model and improve the average predictive skill.

By the end of this course, you’ll learn different techniques for getting better results with deep learning models.

All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Performance-Tuning-Deep-Learning-Models-Master-Class


This course is for developers, machine learning engineers, and data scientists that want to enhance the performance of their deep learning models. This is an intermediate level to advanced level course. It's highly recommended that the learner be proficient in Python, Keras, and machine learning.

A solid foundation in machine learning, deep learning, and Python is required to get better results out of this course. You are also recommended to have the core machine learning libraries in Python.


What will you learn in this course:

  • Introduction to the problem of overfitting and regularization techniques
  • Look at stochastic gradient descent batch size, and other concepts
  • Learn to combat overfitting and an introduction of regularization techniques
  • Reduce overfitting by updating the loss function using techniques
  • Effectively apply dropout, the addition of noise, and early stopping
  • Diagnose high variance in a final model and improve average predictive skill
Performance Tuning Deep Learning in Python - A Masterclass


Check out the detailed breakdown of what’s inside the course

Introduction to the Course
12 Lectures
  • play icon Introduction 01:54 01:54
  • play icon Course Overview 01:49 01:49
  • play icon Is This Course Right for You? 01:07 01:07
  • play icon Course Structure 01:08 01:08
  • play icon Neural Network Defined 02:56 02:56
  • play icon Framework for Optional Learning 02:15 02:15
  • play icon Optimal Generalization Techniques 02:53 02:53
  • play icon Optimal Prediction Techniques 03:27 03:27
  • play icon Framework Application 02:56 02:56
  • play icon Diagnostic Learning Curves 02:56 02:56
  • play icon The Fit of the Model 02:56 02:56
  • play icon Unrepresentative Dataset 01:49 01:49
Optimal Learning
54 Lectures
Optimal Generalization
28 Lectures
Optimal Predictions
21 Lectures

Instructor Details

Packt Publishing

Packt Publishing

Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.

Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.

As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.

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