In the digital age, the ability to categorize and understand images through neural networks is a skill with increasing relevance across various fields. In this course, Neural Networks for Image Classification, you’ll learn to harness the power of neural networks for advanced image classification. First, you’ll explore the fundamentals of image data preparation, feature extraction, and critical steps in creating effective classification models. Next, you’ll discover how to build and evaluate robust image classifiers using TensorFlow, diving into the mechanics of neural network design. Finally, you’ll learn how to amplify your models’ capabilities with advanced architectures such as ResNet, Inception, and MobileNets, employing transfer learning for enhanced performance. When you’re finished with this course, you’ll have the skills and knowledge of neural network-driven image classification needed to apply these techniques in various real-world scenarios.
Author Name: Axel Sirota
Author Description:
Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O’Reilly Media.

Table of Contents

• Course Overview
  1min
• Fundamentals of Image Classification with Neural Networks
  19mins
• Advanced Techniques and Transfer Learning in Image Classification
  14mins