L30: Gradient descent & derivatives | quick recap

Welcome to Lecture 30 of the course "Deep Learning" by Prof. Mitesh M.Khapra Full Course: https://study.iitm.ac.in/ds/course_pa... Video Overview This lecture revisits the concept of gradient descent and introduces its key variants to address common limitations. We begin with a simple one neuron network to refresh foundational ideas and then extend our understanding to deep neural networks using backpropagation. The session highlights how gradient descent behaves in different regions of the loss surface with a specific focus on its slow progress over flat areas. This limitation becomes a starting point for exploring more advanced optimization strategies. In the next part we will dive deeper into the concept of contours to better visualize the optimization process and understand how gradient descent interacts with the geometry of the loss surface. This lecture builds intuition for why plain gradient descent may struggle and lays the groundwork for introducing improved techniques like momentum and adaptive learning rates in future sessions. About IIT Madras' online Bachelor of Science programme IIT Madras offers four-year BS programmes that aim to provide quality education to all, irrespective of age, educational background, or location. The BS programme has multiple levels, which provide flexibility to students to exit at any of these levels. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education), Diploma(s) from IIT Madras, or BSc/BS Degrees from IIT Madras. For more details, Visit: https://www.iitm.ac.in/academics/stud... #gradientdescent #neuralnetworks #backpropagation #optimization #machinelearning #deeplearning #lossfunction #parameters #algorithm #variants #learningrate #derivatives #partials #slope #convergence #contour #flatregions #deepnetworktraining #gradientflow #mloptimization #contouranalysis #slowconvergence #adaptiveoptimization #mlalgorithms #neuraltraining #trainingdynamics #optimizationstrategy #deepgradients #gradientbehavior #mlfundamentals