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 have billions of impacts on the bottom line. Data scientists are constantly working to train, evaluate, iterate, and optimize models to produce highly accurate results and exceptional performance. With NVIDIA’s powerful RAPIDS platform, what used to take days can now be done in minutes, making the creation and deployment of valuable models easier and more flexible. In data science, additional computing power means faster, more effective insights. RAPIDS leverages the power of NVIDIA CUDA to accelerate the entire data science model training workflow and run 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 covered below:
- Using cuDF, cuPy and cuML libraries instead of Pandas, Numpy and scikit-learn. Ensuring data processing and running powerful machine learning algorithms on GPU.
- Performance comparison of classic Python libraries with RAPIDS. In some classroom tests we were able to achieve speedup rates of over 900x. This shows that with special databases and algorithms RAPIDS can be 900 times faster!
- Use RAPIDS to create a complete, step-by-step machine learning project, from data loading to prediction.
- Use DASK to parallelize work across multiple GPUs or CPUs. Integrated with RAPIDS for superior performance.
During the course we will use the Python programming language and Google Colab online. This way you won’t need a local GPU to keep track of the courses as we will be using free hardware from Google.
What you will learn in the AI Application Boost with NVIDIA RAPIDS Acceleration course
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Understand the difference between CPU and GPU computing
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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 with 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 experts want to improve the performance of their applications.
- Professionals currently working or aspiring to work in data science, particularly those looking to improve their skills in machine learning model training and data analysis.
- Anyone interested in machine learning, especially with a focus on high-performance implementations using GPUs
- Professionals involved in the development and implementation of machine learning models
- Students and PhD students working on topics related to artificial intelligence
Course Specifications AI Application Boost with NVIDIA RAPIDS Acceleration
- Editor: Udemy
- Lecturer: 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
Prerequisites for the AI Application Boost with NVIDIA RAPIDS Acceleration Course
- Programming logic
- Basic Python Programming
- Machine Learning: Basic understanding of the algorithm training process and classification and regression techniques
Course pictures
Sample video of the course
installation Guide
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