30-days Money-Back Guarantee
This is a step-by-step course in getting the most out of deep learning models on your own predictive modeling projects.
Updated on Sep, 2026
Programming, Data Science, Python
Duration - 4.5 hours
Training 5 or more people ?
Get your team access to 10000+ top Tutorials Point courses anytime, anywhere.
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.
Check out the detailed breakdown of what’s inside the course
Packt are an established, trusted, and innovative global technical learning publisher, founded in Birmingham, UK with over eighteen years experience delivering rich premium content from ground-breaking authors and lecturers on a wide range of emerging and established technologies for professional development.
Packt’s purpose is to help technology professionals advance their knowledge and support the growth of new technologies by publishing vital user focused knowledge-based content faster than any other tech publisher, with a growing library of over 9,000 titles, in book, e-book, audio and video learning formats, our multimedia content is valued as a vital learning tool and offers exceptional support for the development of technology knowledge.
We publish on topics that are at the very cutting edge of technology, helping IT professionals learn about the newest tools and frameworks in a way that suits them.
Use your certificate to make a career change or to advance in your current career.
Become a valued member of Tutorials Point and enjoy unlimited access to our vast library of top-rated Video Courses
Master prominent technologies at full length and become a valued certified professional.