Inside the mathematics of Gradient Descent.
In the first part of this article, we saw an intuitive understanding of Gradient Descent and some of the concepts required for mathematical understanding of it. In this article, we are going to dive into mathematical details of Gradient Descent. If you have not read the first intuitive part of this blog, I would suggest you to go and read that blog by clicking here.
Outline of this blog:
We are ready to go now,
You must be wondering why we are reading about this optimization thing, the reason is simple, Gradient Descent is an Optimization Algorithm and to understand it we must know what is optimization first. Optimization is in simple terms “The action of making the best or most effective use of a situation or resource.” But in terms of Machine Learning what are those situations or resources? As we use machine learning algorithms to perform some predictive tasks, every algorithm has some loss function to measure the performance of that algorithm for performing a predictive task. This loss function helps us to know how well the machine learning algorithm is predicting the desired output. Lower the loss better is the algorithm, so the aim of any machine learning task is to achieve minimum loss and thus maximum accuracy. Loss is basically an error measurement technique and also known as the cost function. We can call it a function that calculates the difference between the desired output and what our model is predicting. …
What is Gradient Descent in Machine Learning?
Every person who learns/works in the field of Machine Learning comes across one algorithm called Gradient Descent, and we have to admit that Gradient Descent has made the life of algorithms simpler. This article is all about Gradient Descent, here we will see what is exactly Gradient Descent is and How we can use it for simplifying the work of an ML algorithm to get the best results out of it.
Let’s get started,
So as we all know Wikipedia holds most of the information anyone wants to read, and thus Wikipedia has its own definition of the Gradient Descent. …
Can Artificial Intelligence recognize pneumothoraces(Collapsed Lung) from Chest X-ray and save lives?
Artificial intelligence has taken over all kinds of industries, believe it or not, every application you use in your mobile phone is using AI to some extent, there are so many medical treatments which are using AI for the diagnosis of various diseases. In fact, the digital imaging field in healthcare industries is a very popular way for diagnosis of major diseases and nowadays Artificial Intelligence is helping so much for such diagnosis by analyzing the Digital Imaging of X-rays, CT-Scans, etc. …
In this blog, I am showcasing my work on the kaggle problem statement ‘Mercedes-Benz Greener Manufacturing’.
Since the first automobile, the Benz Patent Motor Car in 1886, Mercedes-Benz has stood for important automotive innovations. These include, for example, the passenger safety cell with crumple zone, the airbag, and intelligent assistance systems. Mercedes-Benz applies for nearly 2000 patents per year, making the brand the European leader among premium carmakers. Daimler’s Mercedes-Benz cars are leaders in the premium car industry. With a huge selection of features and options, customers can choose the customized Mercedes-Benz of their dreams.
So before starting Let’s just get the idea of the whole flow of this…
Basic theoretical understanding of Linear Regression.
(Secret: This is a stolen(borrowed) technique from Statistics)
In the current fast growing world lot of technologies are gaining attention. As a result of this many people are moving towards learning the latest technologies to enhance their skillset and ofcourse so as to secure their jobs.
Machine Learning is the fastest growing technology all around the world obviously due to it’s ability to make our lives way easier on daily basis.
Now let’s get to the main objective of our article.
Broadly speaking there are two types of problems we solve in Machine Learning: