NCA-GENM Question 53
Select 3You are developing a multimodal generative AI model that requires both image and text processing. Which of the following deep learning framework features would be most beneficial for this use case?
- A
Support for dynamic computation graphs to handle varying input modalities
- B
Built-in pre-trained models for both vision and natural language processing tasks
- C
GPU acceleration for efficient large-scale training
- D
Limited support for custom data loaders, restricting flexibility
- E
Extensive deployment tools designed specifically for mobile devices
Show answer and explanation
Correct answers: A, B, C
Explanation
Developing a multimodal generative AI model requires a framework that is flexible, efficient, and provides access to pre-trained components. Dynamic computation graphs enable adaptability for handling different data types, while pre-trained models and GPU acceleration ensure efficient and effective development and training. These features make frameworks like PyTorch and TensorFlow particularly well-suited for multimodal AI use cases.
- A. Correct.
Dynamic computation graphs, supported by frameworks like PyTorch, allow for greater flexibility when processing different input modalities such as images and text together.
- B. Correct.
Deep learning frameworks like TensorFlow and PyTorch offer pre-trained models (e.g., ResNet, GPT) that can be fine-tuned for multimodal tasks, significantly reducing development time.
- C. Correct.
GPU acceleration is a core feature of deep learning frameworks and is vital for training large multimodal models efficiently.
- D. Incorrect.
Limited support for custom data loaders would hinder the ability to process diverse datasets, making this feature undesirable in a multimodal scenario.
- E. Incorrect.
While deployment tools are useful, focusing exclusively on mobile-specific tools does not address the broader requirements for multimodal AI development, making this less relevant in the context of this question.