As AI models become increasingly complex, understanding how they arrive at their decisions becomes paramount. In our latest blog post, we delve into the fascinating world of Explainable AI (XAI). We discuss cutting-edge techniques such as Grad-CAM and LIME, which help us visualize and interpret the inner workings of deep learning models. While these tools are powerful, they also have limitations. We explore the challenges and opportunities that lie ahead in the pursuit of transparent and accountable AI. Read Part I now! #AI #MachineLearning #ExplainableAI #XAI #DeepLearning #DataScience
💡Blog Post Alert: Explainability (XAI) techniques for Deep Learning and limitations💡 As AI continues to transform industries, understanding how models make decisions is crucial. In our latest blog, we explore key explainability techniques for deep learning models, including visualization-based methods like Grad-CAM and distillation methods like LIME. While these tools help bridge the gap between complex AI systems and human understanding, challenges like computational complexity and data-specific limitations still remain. Check out Part I of our blog series to learn more about the current state of XAI and the road ahead for AI transparency. Stay tuned for Part II on the opportunities explainability can unlock! Read more 👉 https://lnkd.in/gzqY9pD2 #AI #ExplainableAI #MachineLearning #AITransparency #DeepLearning #AryaXAI
