L38: RMSProp | adaptive learning with exponential decay
Welcome to Lecture 38 of the course "Deep Learning" by Prof. Mitesh M.Khapra Full Course: https://study.iitm.ac.in/ds/course_pa... Video Overview This lecture focuses on RMSProp an adaptive optimization algorithm developed to overcome the rapid and aggressive learning rate decay found in Adagrad. RMSProp improves training stability by maintaining an exponentially decaying average of squared gradients allowing the learning rate to adapt over time without vanishing. This mechanism helps neural networks converge faster while avoiding the risk of premature stagnation during optimization. You will also explore how the choice of initial learning rate can significantly impact the performance of RMSProp and understand the algorithm's sensitivity to hyperparameters. The session includes visual and conceptual insights into how RMSProp balances stability and adaptability making it one of the most effective gradient based optimizers used in deep learning today. 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... #RMSProp #Optimization #MachineLearning #DeepLearning #LearningRate #Adagrad #GradientDescent #ExponentialDecay #Convergence #Algorithms #adaptivelearning #gradientoptimization #mloptimizers #trainingstability #deepnetworktraining #adaptivegradients #optimizerbehavior #efficienttraining #neuralnetworks #losssurface #algorithmcomparison #gradientbasedlearning #aioptimization #mlalgorithms #trainingdynamics #hyperparametersensitivity
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