Coding up Stochastic and Batch Gradient Descent in Python | for Regression
In this lecture, we build on Parts 1 and 2 of the Mastering Gradient Descent lecture. We implement both Stochastic and Batch Gradient descent for the regression problem we set up in Part 2. The implementation is done in python using Jupyter Notebooks. This will give students a comprehensive understanding of how gradient descent works and some of the strengths and pitfalls of the algorithm. Students also get to see the impact of the learning rate and of how noisy data can impact convergence.
This series is taught by Dr Anil Variyar, who has a PhD in Aeronautics and Astronautics from Stanford University.
#gradientdescent #optimization #deeplearning #artificialintelligence #machinelearning #python
Relevant Previous Videos
Part 1: Introduction to Gradient Descent : • Mastering Gradient Descent for Deep Learni...
Part 2: Setting up Gradient Descent for a Regression Problem: • Mastering Gradient Descent for Deep Learni...
Setting up Anaconda and Jupyter Notebooks : Setting up Python with Anaconda and Using Jupyter Notebooks | A Quick Start
Math Prerequisites
Basics of Derivatives : • Basics of Derivatives | A Review | Math fo...
What is Chain Rule : • What is Chain Rule | A Review | Math for AI
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