Udemy – CUDA for Engineers: Accelerating Deep Learning PyTorch 2026

This course teaches AI engineers how to actually accelerate deep learning workloads using CUDA — without drowning in low-level theory. You’ll learn how GPUs work for deep learning, how PyTorch uses CUDA under the hood, how to profile performance bottlenecks, and how to fix them using CUDA, modern PyTorch features, and Triton.

What you’ll learn:

• CUDA fundamentals from an AI engineer’s perspective
• Installing and using CUDA locally, on Colab, and on Kaggle
• Profiling PyTorch models to find real bottlenecks
• GPU acceleration strategies for training and inference
• Mixed precision, Tensor Cores, and modern PyTorch CUDA tools
• Writing custom kernels
• Multi-GPU and distributed training basics

Hands-on projects:

• Accelerate a CNN training pipeline
• Project 2 (changer) :  Distributed Training across multiple GPUs including the different strategies overview and a benchmark of this setting against single GPU setting.
• Project 3 : Distributed Training across multiple GPUs including the different strategies overview and a benchmark of this setting against single GPU setting.