filmov
tv
Few Shot Prompting in Generative AI with Python
Показать описание
Few Shot Prompting in Generative AI with Python
💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇
Prompt engineering is a crucial aspect of generative AI, allowing us to effectively guide model outputs towards specific goals. Few shot prompting, a subset of prompt engineering, enables models to adapt to novel tasks with limited examples. In this video, we'll delve into the concept of few shot prompting and implement it in Python using popular libraries such as Hugging Face Transformers and PyTorch.
Few shot prompting has numerous applications, including natural language processing, computer vision, and reinforcement learning. By optimizing prompts for specific tasks, we can significantly improve model performance and adaptability.
To get the most out of this topic, we suggest exploring the following resources:
* Reading the original research paper on few shot prompting
* Experimenting with different prompt engineering techniques
* Applying few shot prompting to your own projects or research
By mastering few shot prompting, you'll be able to unlock the potential of generative AI and tackle complex problems with ease.
Additional Resources:
None
#stem #artificialintelligence #generativeai #nlp #computervision #reinforcementlearning #pytorch #transformers #huggingface #promptengineering #fewsTOP
Find this and all other slideshows for free on our website:
💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇
Prompt engineering is a crucial aspect of generative AI, allowing us to effectively guide model outputs towards specific goals. Few shot prompting, a subset of prompt engineering, enables models to adapt to novel tasks with limited examples. In this video, we'll delve into the concept of few shot prompting and implement it in Python using popular libraries such as Hugging Face Transformers and PyTorch.
Few shot prompting has numerous applications, including natural language processing, computer vision, and reinforcement learning. By optimizing prompts for specific tasks, we can significantly improve model performance and adaptability.
To get the most out of this topic, we suggest exploring the following resources:
* Reading the original research paper on few shot prompting
* Experimenting with different prompt engineering techniques
* Applying few shot prompting to your own projects or research
By mastering few shot prompting, you'll be able to unlock the potential of generative AI and tackle complex problems with ease.
Additional Resources:
None
#stem #artificialintelligence #generativeai #nlp #computervision #reinforcementlearning #pytorch #transformers #huggingface #promptengineering #fewsTOP
Find this and all other slideshows for free on our website:
0:03:46
0:10:20
0:03:12
0:02:25
0:04:27
0:14:18
0:08:55
0:00:45
0:32:42
0:08:30
0:05:36
0:04:20
0:10:58
0:02:50
0:12:42
0:17:19
0:08:33
0:12:04
0:05:14
0:08:33
0:19:20
0:02:45
0:41:36
0:10:11