L42: AdaGrad: adaptive learning for sparse features
Welcome to Lecture 42 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 the AdaGrad optimization algorithm and explains its key idea of adjusting learning rates for each parameter based on how frequently they are updated during training. AdaGrad is particularly effective in handling sparse features by increasing the learning rate for rarely updated parameters and reducing it for frequently updated ones. You will learn the underlying intuition behind the algorithm, understand its mathematical formulation, and walk through its code implementation. We also visualize AdaGrads behavior on datasets with sparse features and compare its performance against standard gradient descent and momentum based methods. The session concludes with a discussion on the limitations of AdaGrad such as aggressive learning rate decay and how this opens the door to improved optimizers like RMSProp and Adam. 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... #AdaGrad #Optimization #MachineLearning #DeepLearning #GradientDescent #SparseFeatures #LearningRate #AdaptiveLearningRate #Algorithm #Intuition #Code #Tutorial #Momentum #Nestrov #UpdateHistory #Derivatives #Training #AI #ArtificialIntelligence #Mathematics #Calculus #adaptiveoptimizer #gradientupdates #sparsefeaturelearning #deeplearningtraining #parameterwiselearningrate #optimizercomparison #neuralnetworks #trainingalgorithms #introtogradientdescent #learningratedecay #updatehistoryinml #mlmaths #optimizerbehavior
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