null Book

Chapter 1: Describe the NVIDIA software stack used in an AI environment.

Chapter 2: Compare and contrast training and inference architecture requirements and considerations.

Chapter 3: Differentiate the concepts of AI, machine learning, and deep learning.

Chapter 4: Explain the factors contributing to recent rapid improvements and adoption of AI.

Chapter 5: Explain the key AI use cases and industries.

Chapter 6: Explain the purpose and use case of various NVIDIA solutions.

Chapter 7: Describe the software components related to the life cycle of AI development and deployment.

Chapter 8: Compare and contrast GPU and CPU architectures.

Chapter 9: -making.-Understand the process of extracting insights from large datasets using data mining, data visualization, and similar techniques.

Chapter 10: -making.-Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.

Chapter 11: -making.-Conduct data analysis under the supervision of a senior team member.

Chapter 12: -making.-Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.

Chapter 13: -making.-Identify relationships and trends or any factors that could affect the results of research.

Chapter 14: AI Operations: Describe AI data center management and monitoring essentials.

Chapter 15: AI Operations: Describe AI cluster orchestration and job scheduling essentials.

Chapter 16: AI Operations: Articulate the key measures and criteria related to monitoring GPUs.

Chapter 17: AI Operations: Identify the key considerations for virtualizing accelerated infrastructure.

Unlimited Learning, One Price
$299 / INR 23,999