AI Application Boost with NVIDIA RAPIDS Acceleration from Udemy

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

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.

What's inside

Learning objectives

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

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

Syllabus

Introduction

Course content

CPU vs GPU

GPU and CUDA

RAPIDS

Course materials

cuDF

cuDF - intuition

Installation

Pandas and cuDF

Basic commands 1

Basic commands 2

Basic commands 3

Basic commands 4

Integration with cuPy

Other data convertions

User defined functions 1

User defined functions 2

User defined functions 3

Performance comparison 1

Performance comparison 2

Performance comparison 3

cuML

cuML - intution

Preparing the environment

Regression with scikit-learn

Regression with cuML

Ridge regression

Parameter tuning

Complete project

Installations and libraries

Census dataset

Categorical features 1

Categorical features 2

Additional pre-processing

Logistic regression and kNN

Random Forest and SVM

HOMEWORK

Homework solution 1

Homework solution 2

DASK

DASK - intuition

Creating a local cluster

Arrays in distributed GPUs

DASK and cuDF

DASK and cuML 1

DASK and cuML 2

Final remarks

BONUS

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores data science and machine learning, which are the largest computational sectors in the world

Develops in-demand skills, such as building and deploying high-value models

Taught by subject matter experts Jones Granatyr and Gabriel Alves from AI Expert Academy

Provides step-by-step guidance through a complete machine learning project using RAPIDS

Course materials are mostly delivered through Google Colab, which provides learners with free access to Google hardware and eliminates the need for a local GPU

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI Application Boost with NVIDIA RAPIDS Acceleration with these activities:

Review basic Python programming concepts

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Refresh your memory on Python syntax and core concepts to prepare for learning data science and machine learning techniques using RAPIDS.

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Review Python syntax and data types
Practice basic operations using Python

CUDA basics

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Make sure you're familiar with the fundamentals of CUDA before starting this course.

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Implement a parallel for loop
Implement a kernel
Implement a reduction

DASK - getting started

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This will help you ensure that you've set up DASK correctly and avoid common pitfalls in your own projects.

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Install DASK
Run the DASK hello world
Implement a simple DASK pipeline

Three other activities

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Attend a RAPIDS meetup

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This is a great way to connect with other people who are using RAPIDS and learn from their experiences.

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Find a RAPIDS meetup in your area
Attend the meetup
Talk to other people who are using RAPIDS

Contribute to the RAPIDS open source community

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This is a great way to give back to the community and make a difference in the world.

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Find a RAPIDS project that you're interested in contributing to
Create a pull request with your changes
Get your pull request reviewed and merged

Machine learning project with RAPIDS

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This will give you a chance to apply what you've learned in the course to a real-world problem.

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Choose a dataset
Preprocess the data
Build a machine learning model
Evaluate the model

Career center

Learners who complete AI Application Boost with NVIDIA RAPIDS Acceleration will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of data science and machine learning to solve business problems. They work with data to build models that can predict outcomes, identify trends, and make recommendations. Many Data Scientists work on projects such as fraud detection, customer churn prediction, sentiment analysis, and image recognition. This course is likely to be helpful to Data Scientists because it covers topics such as GPU-accelerated processing and parallel and distributed processing.

See salaries and explore the career path for Data Scientist

Data Architect

Data Architects design and build the data infrastructure that supports data science and machine learning initiatives. They work with other stakeholders to ensure that data is accessible, reliable, and secure. This course could be particularly helpful to Data Architects who are interested in using GPU-accelerated processing to improve the efficiency of their work.

See salaries and explore the career path for Data Architect

Machine Learning Researcher

Machine Learning Researchers develop new machine learning algorithms and techniques. They work with other researchers and engineers to develop new products and applications. This course could be particularly helpful to Machine Learning Researchers who are interested in using GPU-accelerated processing to improve the efficiency of their work.

See salaries and explore the career path for Machine Learning Researcher

Artificial Intelligence Researcher

Artificial Intelligence Researchers develop new artificial intelligence algorithms and techniques. They work with other researchers and engineers to develop new products and applications. This course could be particularly helpful to Artificial Intelligence Researchers who are interested in using GPU-accelerated processing to improve the efficiency of their work.

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Machine Learning Engineer

Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. They work closely with data scientists and other stakeholders to ensure that models meet business needs. This course could be particularly helpful to Machine Learning Engineers because it provides hands-on experience with GPU-accelerated machine learning libraries such as cuDF and cuML.

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Business Analyst

Business Analysts help businesses improve their performance by analyzing data and identifying opportunities for improvement. They work with other stakeholders to develop and implement solutions that meet business needs. This course could be particularly helpful to Business Analysts who are interested in using GPU-accelerated processing to improve the efficiency of their work.

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Postdoctoral Researcher

Postdoctoral Researchers conduct research under the supervision of a senior scientist. They work on a variety of projects, including developing new theories and technologies. This course could be particularly helpful to Postdoctoral Researchers who are interested in using GPU-accelerated processing to improve the efficiency of their work.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical methods to help businesses make better decisions. They work on projects such as supply chain optimization, logistics planning, and risk management. This course could be particularly helpful to Operations Research Analysts who are interested in using GPU-accelerated processing to improve the efficiency of their work.

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Statistician

Statisticians collect, analyze, and interpret data. They use statistical methods to draw conclusions about populations and make predictions. This course could be particularly helpful to Statisticians who are interested in using GPU-accelerated processing to improve the efficiency of their work.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions. They work with other analysts and portfolio managers to develop and implement investment strategies. This course could be particularly helpful to Quantitative Analysts who are interested in using GPU-accelerated processing to improve the efficiency of their work.

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Research Scientist

Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. They work with other scientists to develop new theories and technologies. This course could be particularly helpful to Research Scientists who are interested in using GPU-accelerated processing to improve the efficiency of their work.

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Data Engineer

Data Engineers build, maintain, and manage the infrastructure that supports data science and machine learning initiatives. They help ensure that data is accessible, reliable, and secure. Many Data Engineers also work to automate processes and improve efficiency. This course is likely to be helpful to Data Engineers because it covers topics such as GPU-accelerated processing and DASK for task parallelism.

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Data Analyst

Data Analysts leverage their knowledge of data science and machine learning to help businesses make critical decisions about technology, products, and services. Much of the work consists of interpreting large amounts of data and communicating technical insights to non-technical stakeholders. This course could be particularly helpful to Data Analysts because it focuses on accelerating the machine learning process using tools such as cuDF and cuML.

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Computer Programmer

Computer Programmers write and maintain the code that makes software and applications function. They work with other engineers and designers to develop new features and fix bugs. This course could be particularly helpful to Computer Programmers who are interested in developing high-performance computing applications.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They work with other engineers, product managers, and designers to turn ideas into working products. This course could be particularly helpful to Software Engineers who are interested in developing high-performance computing applications.

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