They don’t. First, neural networks are complicated functions, with lots of non-linear transformations thrown in our hypothesis function. The resultant…

Continue Reading# descent

## 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 Reading## It’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 Reading## Gradient 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 Reading## Software 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 Reading## Machine 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 Reading## Understanding 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 Reading## Implementation 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 Reading## Best 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 Reading## Step-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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