Udemy - CUDA for Engineers - Accelerating Deep Learning PyTorch 2026

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.7 GB
  • Uploaded Byfreecoursewb
  • Downloads45
  • Last checkedAug. 09th '26
  • Date uploadedAug. 08th '26
  • Seeders 4
  • Leechers16

Infohash : FE3718FEF5F5AEC0AFE8BDD3D1CD106EBAF80051

CUDA for Engineers: Accelerating Deep Learning PyTorch 2026

https://WebToolTip.com

Published 7/2026
Created by Othmane Kadmiri
MP4 | Video: h264, 1920x1080 | 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

Files:

[ WebToolTip.com ] Udemy - CUDA for Engineers - Accelerating Deep Learning PyTorch 2026
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1 - Introduction
    • 1. Introducing the Course.mp4 (47.3 MB)
    • 2. Part1.Cuda Course.pdf (1.2 MB)
    • 2. Why AI should be fast on GPU.mp4 (37.9 MB)
    • 3. Overview on GPU architectures and it's relation with Accelerations.mp4 (62.5 MB)
    • 4. Cases when to use CUDA Go no-Go use cases.mp4 (27.2 MB)
    2 - CUDA Environment and setup
    • 10. Lab0. Training a Vanilla Network Locally with CUDA.mp4 (16.9 MB)
    • 10. Lab0. Training a Vanilla Network with CUDA.ipynb.bin (4.2 KB)
    • 5. CUDA setup Overview.html (12.9 KB)
    • 5. Part2.Cuda Course.pdf (2.4 MB)
    • 6. Prerequisites for Running CUDA Locally.mp4 (131.9 MB)
    • 6. setting_up_cuda.pdf (302.0 KB)
    • 7. Installing Pytorch For GPU.mp4 (17.1 MB)
    • 8. Setting up CUDA in Google Colab.mp4 (32.4 MB)
    • 9. Setting up CUDA in Kaggle.mp4 (37.0 MB)
    3 - CUDA Fundamentals
    • 1. CUDA Fundamentals.html (21.0 KB)
    • 11. Overview.html (11.5 KB)
    • 12. Cuda Programming Model Overview.mp4 (22.3 MB)
    • 12. Part3. Cuda Fundamentals.pdf (1.7 MB)
    • 13. Cuda Heterogenous Computing.mp4 (8.2 MB)
    • 14. Writing Your First CUDA Kernel The 6-Step Execution Flow.mp4 (24.8 MB)
    • 15. Lab 1.1 Writing a Cuda Kernel For Vector Addition in Google Colab.mp4 (39.4 MB)
    • 16. Parallel Computing Cuda Blocks, Grids, Threads and Indexing.mp4 (55.0 MB)
    • 17. Lab 1.2 Implementing Threads, Blocks and Grids concept into our CUDA code.mp4 (31.9 MB)
    • 18. Lab 1.3 Understanding Thread Indexing and Recapitulation.mp4 (23.3 MB)
    • 2. Cuda Fundamentals Part 2.html (21.0 KB)
    • Lab 1 Resources and Correction.html (1.4 KB)
    • Lab1. Cuda_Kernels_With_Google_Colab.ipynb (28.8 KB)
    • Lab1cor_Cuda_Kernels_With_Google_Colab.ipynb (31.3 KB)
    • Parallel Matrix Multiplication.html (2.5 KB)
    4 - Profiling & Performance Analysis
    • 19. Module Overview.html (11.5 KB)
    • 20. Why do we need profiling.mp4 (16.0 MB)
    • 21. Profiling and typical bottlenecks.mp4 (20.3 MB)
    • 22. Pytorch Profiler.mp4 (79.6 MB)
    • 23. Lab 2 Profiling With Tensorboard.mp4 (69.3 MB)
    • 23. Lab2_tensorboard_with_pytorch.ipynb.bin (9.0 MB)
    • 24. CLI Monitoring.html (14.0 KB)
    • 25. Nvidia Nsight Profiler.html (17.3 KB)
    5 - Pytorch With CUDA
    • 26. Module Overview.html (11.2 KB)
    • 27. Cuda With Pytorch.ipynb.bin (51.6 KB)
    • 27. Cuda With Pytorch.pdf (168.4 KB)
    • 27. Programing fundamentals of CUDA using Pytorch.mp4 (65.4 MB)
    • 28. Lab 3 Executing CUDA programs and commands with Pytorch.html (7.5 KB)
    • 29. Lab 3.1 Generalities, Device Informations, Tensor Operations.mp4 (96.9 MB)
    • 30. Lab 3.2 Neural Networks on GPU.mp4 (108.1 MB)
    • 31. Lab 3.3 Memory Management.mp4 (56.7 MB)
    • 32. Lab 3.4 Profiling a Neural Network.mp4 (102.0 MB)
    • 33. Best Practices.html (13.0 KB)
    6 - Project 1 Acceleration of a CNN for Multiclass Classification
    • 34. Project Introduction.html (12.8 KB)
    • 35. Project Overview.mp4 (25.8 MB)
    • 35. Project1local. Accelerating a CNN on CIFAR dataset.ipynb.bin (439.6 KB)
    • 36. First Speedup Moving Computation to GPU.mp4 (61.6 MB)
    • 37. Profiling and Performance Analysis.html (16.1 KB)
    • 38. Understanding the Host-to-Device Transfer Bottleneck.html (13.6 KB)
    • 39. Pinning Memory and Asynchronous transfers.mp4 (82.3 MB)
    • 40. Data Prefetching.html (14.4 KB)
    • 41. The Impact of Batch Size.mp4 (35.7 MB)
    • 42. Multiprocess Data Loading.mp4 (80.7 MB)
    • 43. Mixed Precision Training (AMP).mp4 (60.0 MB)
    • 43. Part6.7. Automatic Mixed Precision.pdf (275.1 KB)
    • 44. Implementing and Benchmarking AMP.mp4 (78.3 MB)
    • 45. Extend the Project.html (12.6 KB)
    7 - Project 2 Acceleration of a MobileNet for embedded Computer Vision
    • 46. End To End Project Tutorial.html (15.2 KB)
    • Implementation exercice Accelerating a MobileNet for embedded Computer Vision.html (3.3 KB)
    • Project2. Mobilenet_CIFAR10_Acceleration.ipynb (953.8 KB)
    • Project2Correction. Mobilenet_CIFAR10_Acceleration copy.ipynb (958.3 KB)
    8 - Project 3 Introduction To Distributed Training With TensorFlow
    • 47. End To End Project Tutorial.html (13.1 KB)
    • 48. Data parallelism vs Model Parallelism.mp4 (46.6 MB)
    • Bonus Resources.txt (0.1 KB)

Code:

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