Evaluating Finetuning (11.4)
About this lesson
In this video, we focus on evaluating the performance of generative AI models, utilizing techniques from the T81-559 course notebook. We walk through key methods for assessing model outputs, including quantitative and qualitative evaluation metrics. By the end of the tutorial, you'll understand how to measure the effectiveness of your AI models, fine-tune them for better results, and ensure they meet the desired performance criteria. This is essential for AI practitioners aiming to optimize model accuracy and reliability in real-world applications. Code for This Video: https://github.com/jeffheaton/app_generative_ai/blob/main/t81_559_class_11_4_eval.ipynb ~~~~~~~~~~~~~~~ COURSE MATERIAL ~~~~~~~~~~~~~~~ 📖 Textbook - Coming soon 😸🐙 GitHub - https://github.com/jeffheaton/app_generative_ai ▶️ Play List - https://www.youtube.com/watch?v=FBmUxUt__rM&list=PLjy4p-07OYzui0nVZzMgoLBeXjG9Oy3hi&ab_channel=JeffHeaton ~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~ 🖥️ Website: https://www.heatonresearch.com/ 🐦 Twitter - https://twitter.com/jeffheaton 😸🐙 GitHub - https://github.com/jeffheaton 📸 Instagram - https://www.instagram.com/jeffheatondotcom/ 🦾 Discord: https://discord.gg/3bjthYv ▶️ Subscribe: https://www.youtube.com/c/heatonresearch?sub_confirmation=1 ~~~~~~~~~~~~~~ SUPPORT ME 🙏~~~~~~~~~~~~~~ 🅿 Patreon - https://www.patreon.com/jeffheaton 🙏 Other Ways to Support (some free) - https://www.heatonresearch.com/support.html ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #GenerativeAI #AIEvaluation #ModelPerformance #MachineLearning #T81_559 #AIOptimization #ModelTesting #AIAccuracy #DeepLearning
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