Description
AI Application Boost with NVIDIA RAPIDS Acceleration course. Data science and machine learning represent the largest computing sectors in the world, where small improvements in the accuracy of analytical models can translate into billions of impacts on the bottom line. Data scientists are constantly trying 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 done in minutes, making it easier and more agile to build and deploy valuable models. In data science, additional computing power means faster and more effective insights. RAPIDS leverages the power of NVIDIA CUDA to accelerate the entire data science model training workflow and executes it on graphics processing units (GPUs). In this course, you’ll learn everything you need to take your machine learning programs to the next level! Check out some of the topics that will be covered below:
- Using cuDF, cuPy and cuML libraries instead of Pandas, Numpy and scikit-learn. Ensuring data processing and running high-performance machine learning algorithms on the GPU.
- Performance comparison of classical Python libraries with RAPIDS. In some tests conducted during classes, we achieved acceleration rates of over 900x. This shows that with special databases and algorithms, RAPIDS can be 900 times faster!
- Create a complete, step-by-step machine learning project using RAPIDS, from data loading to prediction.
- Using DASK to parallelize work on multiple GPUs or CPUs. Integrated with RAPIDS for superior performance.
During the course, we will use Python programming language and Google Colab online. That way, you don’t need to have a local GPU to keep track of the classes, as we’ll be using free hardware provided by Google.
What you will learn in AI Application Boost with NVIDIA RAPIDS Acceleration course
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Understand the difference between processing data using CPU and GPU
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Use cuDF as an alternative to pandas for GPU-accelerated processing
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Implement code using cuDF to manipulate DataFrames
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Use cuPy as an alternative to numpy for GPU-accelerated processing
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Use cuML as an alternative to scikit-learn for GPU-accelerated processing
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Run a complete machine learning project using cuDF and cuML
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Performance comparison of classic Python libraries running on the CPU with RAPIDS libraries running on the GPU.
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Running projects with DASK for parallel and distributed processing
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Integrate DASK with cuDF and cuML for GPU performance
This course is suitable for people who
- Data scientists and AI professionals are looking to improve the performance of their applications
- Professionals currently working or aspiring to work in data science, especially those looking to improve their skills in training machine learning models and data analysis.
- Anyone interested in learning about machine learning, particularly with a focus on high-performance implementations using GPUs
- Specialists involved in the development and implementation of machine learning models
- Undergraduate and graduate students studying topics related to artificial intelligence
Course specifications AI Application Boost with NVIDIA RAPIDS Acceleration
- Publisher: Udemy
- teacher: Jones Granatyr
- Training level: beginner to advanced
- Training duration: 6 hours and 21 minutes
- Number of courses: 46
Headlines of the course on 2/2024
AI Application Boost with NVIDIA RAPIDS Acceleration course prerequisites
- Programming logic
- Basic Python programming
- Machine learning: basic understanding of the algorithm training process, as well as classification and regression techniques
Course images
Sample video of the course
Installation guide
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download link
File(s) password: www.downloadly.ir
Size
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