AI Application Boost with NVIDIA RAPIDS Acceleration

AI Application Boost with NVIDIA RAPIDS Acceleration, available at $54.99, has an average rating of 4.82, with 47 lectures, based on 50 reviews, and has 660 subscribers.

You will learn about 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 This course is ideal for individuals who are Data scientists and artificial intelligence professionals looking to enhance the performance of their applications or Professionals currently working or aspiring to work in the field of data science, particularly those seeking to improve their skills in machine learning model training and data analysis or Anyone interested in learning about machine learning, especially with a focus on high-performance implementations using GPUs or Professionals involved in the development and implementation of machine learning models or Undergraduate and graduate students studying subjects related to artificial intelligence It is particularly useful for Data scientists and artificial intelligence professionals looking to enhance the performance of their applications or Professionals currently working or aspiring to work in the field of data science, particularly those seeking to improve their skills in machine learning model training and data analysis or Anyone interested in learning about machine learning, especially with a focus on high-performance implementations using GPUs or Professionals involved in the development and implementation of machine learning models or Undergraduate and graduate students studying subjects related to artificial intelligence.

Enroll now: AI Application Boost with NVIDIA RAPIDS Acceleration

Summary

Title: AI Application Boost with NVIDIA RAPIDS Acceleration

Price: $54.99

Average Rating: 4.82

Number of Lectures: 47

Number of Published Lectures: 47

Number of Curriculum Items: 47

Number of Published Curriculum Objects: 47

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Data scientists and artificial intelligence professionals looking to enhance the performance of their applications
Professionals currently working or aspiring to work in the field of data science, particularly those seeking to improve their skills in machine learning model training and data analysis
Anyone interested in learning about machine learning, especially with a focus on high-performance implementations using GPUs
Professionals involved in the development and implementation of machine learning models
Undergraduate and graduate students studying subjects related to artificial intelligence

Target Audiences

Data scientists and artificial intelligence professionals looking to enhance the performance of their applications
Professionals currently working or aspiring to work in the field of data science, particularly those seeking to improve their skills in machine learning model training and data analysis
Anyone interested in learning about machine learning, especially with a focus on high-performance implementations using GPUs
Professionals involved in the development and implementation of machine learning models
Undergraduate and graduate students studying subjects related to artificial intelligence

Data science and machine learning represent the largest computational sectors in the world, where modest improvements in the accuracy of analytical models can translate into billions of impact on the bottom line. Data scientists are constantly striving to train, evaluate, iterate, and optimize models to achieve highly accurate results and exceptional performance. With NVIDIA’s powerful RAPIDS platform, what used to take days can now be accomplished in a matter of minutes, making the construction and deployment of high-value models easier and more agile. In data science, additional computational power means faster and more effective insights. RAPIDS harnesses the power of NVIDIA CUDA to accelerate the entire data science model training workflow, running it on graphics processing units (GPUs).

In this course, you will learn everything you need to take your machine learning applications to the next level! Check out some of the topics that will be covered below:

Utilizing the cuDF, cuPy, and cuML libraries instead of Pandas, Numpy, and scikit-learn; ensuring that data is processed and machine learning algorithms are executed with high performance on the GPU.
Comparing the performance of classic Python libraries with RAPIDS. In some experiments conducted during the classes, we achieved acceleration rates exceeding 900x. This indicates that with certain databases and algorithms, RAPIDS can be 900 times faster!
Creating a complete, step-by-step machine learning project using RAPIDS, from data loading to predictions.
Using DASK for task parallelism on multiple GPUs or CPUs; integrated with RAPIDS for superior performance.

Throughout the course, we will use the Python programming language and the online Google Colab. This way, you don’t need to have a local GPU to follow the classes, as we will use the free hardware provided by Google.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course content

Lecture 2: CPU vs GPU

Lecture 3: GPU and CUDA

Lecture 4: RAPIDS

Lecture 5: Course materials

Chapter 2: cuDF

Lecture 1: cuDF – intuition

Lecture 2: Installation

Lecture 3: Pandas and cuDF

Lecture 4: Basic commands 1

Lecture 5: Basic commands 2

Lecture 6: Basic commands 3

Lecture 7: Basic commands 4

Lecture 8: Integration with cuPy

Lecture 9: Other data convertions

Lecture 10: User defined functions 1

Lecture 11: User defined functions 2

Lecture 12: User defined functions 3

Lecture 13: Performance comparison 1

Lecture 14: Performance comparison 2

Lecture 15: Performance comparison 3

Chapter 3: cuML

Lecture 1: cuML – intution

Lecture 2: Preparing the environment

Lecture 3: Regression with scikit-learn

Lecture 4: Regression with cuML

Lecture 5: Ridge regression

Lecture 6: Parameter tuning

Lecture 7: Performance comparison 1

Lecture 8: Performance comparison 2

Lecture 9: Performance comparison 3

Chapter 4: Complete project

Lecture 1: Installations and libraries

Lecture 2: Census dataset

Lecture 3: Categorical features 1

Lecture 4: Categorical features 2

Lecture 5: Additional pre-processing

Lecture 6: Logistic regression and kNN

Lecture 7: Random Forest and SVM

Lecture 8: HOMEWORK

Lecture 9: Homework solution 1

Lecture 10: Homework solution 2

Chapter 5: DASK

Lecture 1: DASK – intuition

Lecture 2: Creating a local cluster

Lecture 3: Arrays in distributed GPUs

Lecture 4: DASK and cuDF

Lecture 5: DASK and cuML 1

Lecture 6: DASK and cuML 2

Chapter 6: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor
Gabriel Alves
Developer
AI Expert Academy
Instructor

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 14 votes
5 stars: 35 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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