They don’t. First, neural networks are complicated functions, with lots of non-linear transformations thrown in our hypothesis function. The resultant…
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An overview of the Gradient Descent algorithm
That explains why the least squared loss works for a wide range of problems. The underlying noise is very often…
Continue ReadingIt’s Only Natural: An Excessively Deep Dive Into Natural Gradient Optimization
The premise of having a small learning rate is that we know that a single local estimate of gradient may…
Continue ReadingGradient Descent for Machine Learning
We can use the same equation in order to represent the regression line in computer. If you can’t recall it,…
Continue ReadingSoftware 2.0 —Deep dive with Neural Networks (Part 2)
We use the plot between number of iterations and the loss/error described by the cost function:Note: The x-axis is the…
Continue ReadingMachine Learning From Scratch: Logistic Regression
For instance, we could, depending on our projects’ requirements, set Y=0 if P≤0.5 and Y=1 if P>0.5.All that’s left to…
Continue ReadingUnderstanding the 3 Primary Types of Gradient Descent
Mini Batch Gradient Descent is commonly used for deep learning problems.ConclusionThis article should give you the basic motivation for the…
Continue ReadingImplementation of Uni-Variate Linear Regression in Python using Gradient Descent Optimization from scratch
The implementation of hypothesis() remains the same.=>BGD(): It is the function that performs the Batch Gradient Descent Algorithm taking current…
Continue ReadingBest Optimization Gradient Descent Algorithm
The most common is the Mean-Squared Error cost function.This formula shows the gradient computation for linear regression with respect to…
Continue ReadingStep-by-Step Tutorial on Linear Regression with Stochastic Gradient Descent
6: Updating the weights and bias (dark green nodes)Also pay attention to the ‘direction’ of the pathway from the yellow node…
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