L19: Learning parameters: taylor series approximation | navigating error surfaces
Welcome to Lecture 19 of the course "Deep Learning" by Prof. Mitesh M.Khapra Full Course: https://study.iitm.ac.in/ds/course_pa... Video Overview This lecture explains how to effectively navigate the error surface to minimize the error in your machine learning models. Using a toy network as an example, we introduce the Taylor series approximation to understand how small changes in parameters like weights and bias can guide us toward the point of minimum error. This principled approach helps you move smoothly across the error surface and efficiently reach the optimal parameter values for your model. This session will build your intuition for using mathematical tools in optimization and prepare you for deeper understanding of gradient descent in neural network training. 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... #MachineLearning #DeepLearning #ErrorSurface #ParameterEstimation #TaylorSeries #GradientDescent #Optimization #ArtificialIntelligence #NeuralNetworks #LossFunction #LearningRate #ModelTraining #Backpropagation #ConvexOptimization #MLTheory #AIModels #NeuralComputation #TrainingDynamics #MathForML #FunctionApproximation
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