Udemy - AI Application Boost with NVIDIA RAPIDS Acceleration

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size2.4 GB
  • Uploaded Byfreecoursewb
  • Downloads25
  • Last checkedMar. 03rd '24
  • Date uploadedMar. 02nd '24
  • Seeders 8
  • Leechers14
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Infohash : FF9B3C67F56018789780FEE4E8E536A0B1CFB6BD

AI Application Boost with NVIDIA RAPIDS Acceleration

https://DevCourseWeb.com

Published 2/2024
Created by Jones Granatyr
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 46 Lectures ( 6h 21m ) | Size: 2.4 GB

High-speed and high-performance GPU and CUDA computing! Build Data Science pipelines 50 times faster!

What you'll learn:
Understand the differences between processing data using CPU and GPU
Use cuDF as a replacement for pandas for GPU-accelerated processing
Implement codes using cuDF to manipulate DataFrames
Use cuPy as a replacement for numpy for GPU-accelerated processing
Use cuML as a replacement for scikit-learn for GPU-accelerated processing
Implement a complete machine learning project using cuDF and cuML
Compare the performance of classic Python libraries that run on the CPU with RAPIDS libraries that run on the GPU
Implement projects with DASK for parallel and distributed processing
Integrate DASK with cuDF and cuML for GPU performance

Requirements:
Programming logic
Basic Python programming
Machine learning: basic understanding of the algorithm training process, as well as classification and regression techniques

Files:

[ DevCourseWeb.com ] Udemy - AI Application Boost with NVIDIA RAPIDS Acceleration
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. Course content.mp4 (85.2 MB)
    • 2. CPU vs GPU.mp4 (50.3 MB)
    • 3. GPU and CUDA.mp4 (55.5 MB)
    • 4. RAPIDS.mp4 (55.5 MB)
    • 5. Course materials.html (0.2 KB)
    2. cuDF
    • 1. cuDF - intuition.mp4 (35.5 MB)
    • 1.1 Source code - Google Colab.html (0.1 KB)
    • 10. User defined functions 1.mp4 (122.1 MB)
    • 11. User defined functions 2.mp4 (59.8 MB)
    • 12. User defined functions 3.mp4 (27.2 MB)
    • 13. Performance comparison 1.mp4 (52.2 MB)
    • 14. Performance comparison 2.mp4 (82.6 MB)
    • 15. Performance comparison 3.mp4 (45.0 MB)
    • 2. Installation.mp4 (82.1 MB)
    • 3. Pandas and cuDF.mp4 (33.3 MB)
    • 4. Basic commands 1.mp4 (47.5 MB)
    • 5. Basic commands 2.mp4 (47.7 MB)
    • 6. Basic commands 3.mp4 (62.2 MB)
    • 7. Basic commands 4.mp4 (74.1 MB)
    • 8. Integration with cuPy.mp4 (38.3 MB)
    • 9. Other data convertions.mp4 (45.4 MB)
    3. cuML
    • 1. cuML - intution.mp4 (32.1 MB)
    • 1.1 Source code - Google Colab.html (0.1 KB)
    • 2. Preparing the environment.mp4 (56.4 MB)
    • 3. Regression with scikit-learn.mp4 (57.0 MB)
    • 4. Regression with cuML.mp4 (67.2 MB)
    • 5. Ridge regression.mp4 (104.8 MB)
    • 6. Parameter tuning.mp4 (54.5 MB)
    • 7. Performance comparison 1.mp4 (57.8 MB)
    • 8. Performance comparison 2.mp4 (42.2 MB)
    4. Complete project
    • 1. Installations and libraries.mp4 (14.5 MB)
    • 1.1 Source code - Google Colab.html (0.1 KB)
    • 10. Homework solution 2.mp4 (47.5 MB)
    • 2. Census dataset.mp4 (50.1 MB)
    • 3. Categorical features 1.mp4 (47.5 MB)
    • 4. Categorical features 2.mp4 (39.8 MB)
    • 5. Additional pre-processing.mp4 (50.1 MB)
    • 6. Logistic regression and kNN.mp4 (69.9 MB)
    • 7. Random Forest and SVM.mp4 (34.8 MB)
    • 8. HOMEWORK.html (1.7 KB)
    • 9. Homework solution 1.mp4 (105.4 MB)
    • 9.1 Source code - Google Colab.html (0.1 KB)
    5. DASK
    • 1. DASK - intuition.mp4 (67.9 MB)
    • 1.1 Source code - Google Colab.html (0.1 KB)
    • 2. Creating a local cluster.mp4 (57.4 MB)
    • 3. Arrays in distributed GPUs.mp4 (56.2 MB)
    • 4. DASK and cuDF.mp4 (47.9 MB)
    • 5. DASK and cuML 1.mp4 (93.3 MB)
    • 6. DASK and cuML 2.mp4 (69.0 MB)
    6. Final remarks
    • 1. Final remarks.mp4 (7.7 MB)
    • 2. BONUS.mp4 (29.4 MB)
    • Bonus Resources.txt (0.4 KB)

Code:

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