The efficiency and performance of neural networks are important for fast, lightweight, and energy-efficient AI solutions. In this course, Optimizing Neural Networks for Efficient Data Processing, you’ll gain the ability to optimize neural networks to achieve better performance with less computational resources and energy consumption. First, you’ll explore the fundamentals of neural network efficiency, covering topics like weight initialization and optimization algorithms to start your models in the right way. Next, you’ll discover regularization techniques to improve model generalization and prevent overfitting, ensuring your models are robust and reliable. Finally, you’ll learn how to apply model pruning and quantization techniques to significantly reduce model size and improve speed, making your models ideal for deployment. When you’re finished with this course, you’ll have the skills and knowledge of neural networks optimization needed to create efficient and high-performing AI models.
Author Name: Alper Tellioglu
Author Description:
I am a developer, and passionate about technology in all kind. That’s why I worked on several development areas in the past, and still exploring other technologies and frameworks that catch my interest. Besides engineering, I am interested in design and art. I like building things with small and dynamic teams. Working in startup environment keeps me motivated!

Table of Contents

• Course Overview
  1min
• Fundamentals of Neural Network Efficiency
  17mins
• Hands-on Optimizing Neural Networks
  15mins