Deep Learning with PyTorch | S3P1 | Understanding Gradient Descent Optimization

This video is a part of the deep learning foundations course using PyTorch. In this video, I have given you an introduction to the gradient descent optimization process. In this process, the model parameter values are tweaked in order to reduce the overall loss. GitHub Repository: https://github.com/bijoyandas/Deep-Learning-Foundation-PyTorch Complete Playlist: https://www.youtube.com/playlist?list=PLMQ4k-hUWGNlvT1VeJ8ybAh2KVZb-Ugxu Follow me on LinkedIn: https://www.linkedin.com/in/bijoyandas/ Timecodes: 0:00 - Intro 0:43 - Recap of Neural Networks 2:24 - The Regression Problem 3:28 - Neuron for Linear Regression 5:01 - Fitting the best line 8:07 - What is Gradient Descent 9:43 - Visualizing Gradient Descent 14:00 - Conclusion If you have any queries related to the video, please reach out to me in the comment section below.