100% satisfaction guarantee Immediately available after payment Both online and in PDF No strings attached
Previously searched by you
(ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output va$7.99
Add to cart
(ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output va
2 views 0 purchase
Course
AL3451
Institution
KCG College Of Technology
(ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
, Gradient Descent in Machine Learning
Gradient Descent is known as one of the most commonly used optimization algorithms to train
machine learning models by means of minimizing errors between actual and expected results.
Further, gradient descent is also used to train Neural Networks.
A gradient measures how much the output of a function changes if you change the inputs a little bit.
In mathematical terminology, Optimization algorithm refers to the task of minimizing/maximizing
an objective function f(x) parameterized by x.
Similarly, in machine learning, optimization is the task of minimizing the cost function parameterized
by the model's parameters.
The main objective of gradient descent is to minimize the convex function using iteration of parameter updates.
Once these machine learning models are optimized, these models can be used as powerful tools for
Artificial Intelligence and various computer science applications.
Gradient descent finds the nearest minimum of a function(minimize the particular function),
Gradient ascent finds the nearest maximum.(maximize the particular function)
What is Gradient Descent or Steepest Descent?
Gradient descent was initially discovered by "Augustin-Louis Cauchy" in mid of 18th century.
Gradient Descent is defined as one of the most commonly used iterative optimization algorithms of
machine learning to train the machine learning and deep learning models.
It helps in finding the local minimum of a function.
The best way to define the local minimum or local maximum of a function using gradient descent is as follows:
o If we move towards a negative gradient or away from the gradient of the function at the current point, it will
give the local minimum of that function.
o Whenever we move towards a positive gradient or towards the gradient of the function at the current point,
we will get the local maximum of that function.
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller hariharansv1810. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $7.99. You're not tied to anything after your purchase.