AI Application Boost with NVIDIA RAPIDS Acceleration

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

Created by: Jones Granatyr
Professor

Created by: Gabriel Alves
Developer

Created by: AI Expert Academy
Instructor

What Will I 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

Requirements

• Programming logic
• Basic Python programming
• Machine learning: basic understanding of the algorithm training process, as well as classification and regression techniques

Target audience

• 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