本课程专为AI工程师设计，深入PyTorch与CUDA底层，打破加速误区。涵盖性能分析、混合精度及分布式训练，通过3个动手项目实战CNN与多GPU，实现大模型时代低延迟高吞吐加速。
原始标题：CUDA for Engineers: Accelerating Deep Learning PyTorch 2026
本课程专注于PyTorch与CUDA底层的GPU加速实战，旨在帮助具备深度学习和Python基础的AI工程师打破“仅靠.to('cuda')就能加速”的误区。课程通过本地及云端环境搭建、Torch Profiler性能分析、混合精度（FP16/BF16）与张量核心（Tensor Cores）应用，指导学员深入理解并解决深度学习模型在GPU上的计算与内存瓶颈。核心内容涵盖3个包含前后性能对比基准（Benchmark）的动手项目，带领学员攻克从CNN训练管线加速、多GPU分布式训练（DDP/FSDP）到使用Triton编写自定义高性能算子（Kernel）的完整技术栈，最终实现大模型时代的低延迟、高吞吐深度学习加速。
Published 7/2026
Created by Othmane Kadmiri
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 48 Lectures ( 4h 5m ) | Size: 1.7 GB
GPU Acceleration of Deep Neural Networks using Pytorch and CUDA with 3 Hands-on Projects
What you’ll learn
⚡ Setting Up CUDA locally or with Cloud GPU Provider
⚡ Understanding and applying CUDA programming fundamentals
⚡ Build and optimize GPU-accelerated applications in PyTorch
⚡ Profile and analyze deep learning models using professional tools such as Torch Profiler to identify performance bottlenecks
⚡ Introduction to Distributed Training With TensorFlow
⚡ 3 Hands-on Interactive projects
Requirements
❗ Deep Learning Basics
❗ Python Programming
Description
PyTorch models don’t become fast just by moving them to the GPU.
This course teachesAI engineers how toactually 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 usingCUDA, modern PyTorch features, and Triton.
What you’ll learn
✨ CUDA fundamentals from anAI 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.
Each project includesbefore/after benchmarks.
Who this course is for
⭐ AI and Machine Learning Engineers, Data Scientists, Software Engineers
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