Скачать или смотреть How to Build a Deep Learning Model for Computer Vision Explained by Prof Horst Bischof

Building a deep learning model for computer vision begins long before writing code. In this segment of the Prof. Mahesh Podcast, Prof. Horst Bischof explains the complete pipeline—from defining the problem to testing the final model in the real world.

He breaks down why problem definition is the first critical step, and why large, well-labeled datasets are essential for any successful deep learning system. The conversation also covers how data is collected and annotated, how neural network architectures are chosen, and why modern tools make experimentation easier than ever.

You’ll learn:

Why defining the problem is the most important first step
How to collect, label, or auto-label massive datasets
Why deep learning fails without enough data
How to choose and tune neural network architectures
Why compute power is essential for training large models
How real-world testing validates a computer vision system

A clear and practical introduction to the full workflow behind modern computer vision systems—and what students must understand before building AI models.