AI Application Boost with NVIDIA RAPIDS Acceleration

سرفصل های دوره

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

1. Introduction

1. Course content

2. CPU vs GPU

3. GPU and CUDA

4. RAPIDS

5. Course materials.html

2. cuDF

1.1 Source code - Google Colab.html

1. cuDF - intuition

2. Installation

3. Pandas and cuDF

4. Basic commands 1

5. Basic commands 2

6. Basic commands 3

7. Basic commands 4

8. Integration with cuPy

9. Other data convertions

10. User defined functions 1

11. User defined functions 2

12. User defined functions 3

13. Performance comparison 1

14. Performance comparison 2

15. Performance comparison 3

3. cuML

1.1 Source code - Google Colab.html

1. cuML - intution

2. Preparing the environment

3. Regression with scikit-learn

4. Regression with cuML

5. Ridge regression

6. Parameter tuning

7. Performance comparison 1

8. Performance comparison 2

4. Complete project

1.1 Source code - Google Colab.html

1. Installations and libraries

2. Census dataset

3. Categorical features 1

4. Categorical features 2

5. Additional pre-processing

6. Logistic regression and kNN

7. Random Forest and SVM

8. HOMEWORK.html

9.1 Source code - Google Colab.html

9. Homework solution 1

10. Homework solution 2

5. DASK

1.1 Source code - Google Colab.html

1. DASK - intuition

2. Creating a local cluster

3. Arrays in distributed GPUs

4. DASK and cuDF

5. DASK and cuML 1

6. DASK and cuML 2

6. Final remarks

1. Final remarks

2. BONUS

139,000 تومان

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تولید کننده: Udemy