100 Days of Deep Learning - Optimizers in Deep Learning | Part 1 | Complete Deep Learning Course

This video breaks down the key algorithms that fine-tune neural network parameters for optimal performance. From classic techniques like Gradient Descent to advanced methods like Adam and RMSprop, explore how optimizers play a crucial role in training models effectively. Watch to gain insights into choosing the right optimizer for your deep learning tasks and enhancing model convergence. Notes: https://learnwith.campusx.in/s/store/courses/YouTube%20Notes ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 👍If you find this video helpful, consider giving it a thumbs up and subscribing for more educational videos on data science! 💭Share your thoughts, experiences, or questions in the comments below. I love hearing from you! ⌚Time Stamps⌚ 00:00 - Intro 00:28 - Intro to Optimizers 02:33 - Role Of optimizers 08:00 -Types Of Optimizers 11:08 - The Challenges? 21:06 - What next?