Deep Learning for Computer Vision - A Practitioner's Guide
Build explainable, deployment-ready vision systems for industrial applications
Created by Barak Or
Gain the skills to design, evaluate, and deploy computer vision systems that work reliably in real-world industrial settings. Explore core models like CNNs and Vision Transformers, dive into detection and segmentation, and learn to optimize for edge devices and explainability.
Packt | Aug 2026 | 157 min
What You Will Learn
You will start with foundational concepts like convolutional networks and core vision tasks, then move through advanced topics such as Vision Transformers, detection, segmentation, and generative models. Each section connects architecture choices to practical system constraints, helping you make informed decisions for industrial deployment.
Key Features
- Build robust vision pipelines using CNNs, Vision Transformers, and generative models
- Optimize models for edge deployment while balancing accuracy, latency, and memory
- Interpret predictions and manage explainability, robustness, and lifecycle reliability
Target Audience
Ideal for engineers, data scientists, and AI practitioners who build or deploy computer vision systems. If you have a working knowledge of Python, PyTorch, and basic deep learning, you will benefit from practical guidance on reliability, explainability, and production deployment in industrial AI environments.
