#analyticssteps

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L2 vs L1 Regularization in Machine Learning | Ridge and Lasso Regularization

L2 and L1 regularization are the well-known techniques to reduce overfitting in machine learning models.

[neelamtyagi]

5 Clustering Methods and Applications

Link of the blog: https://www.analyticssteps.com/blogs/5-clustering-methods-and-applications

[neelamtyagi]

5 Clustering Methods in Machine Learning | Clustering Applications

Clustering is a potent machine learning tool that detects structures in datasets, describing the notable clustering methods and applications through the blog.

Clustering is the most powerful tool in machine learning for capturing structures in extensive datasets, it has various real-life applications where it can be used in a variety of situations. You can learn more about such applications and methods in the blog.

[neelamtyagi]

Generative Adversarial Network (GAN) in Unsupervised Machine Learning | Analytics Steps

GANs are neural networks in unsupervised machine learning used for generative modeling that entails a model to compose new samples mapped from the existing population of data instances.

“It’s all about legit”, legit to images and voice generation, discover how?

GAN is about creating a portrait or composing ritornello whether you want to create pictures of any celebrity or data in data-limited solutions. GANs are a simple solution to complex problems that are hard to compare with other deep learning realms. 

Explore how two neural networks train each other through the adversarial process, a branch of unsupervised learning technique in machine learning. 

[neelamtyagi]

How does the Internet of Things sketch a smart city under the IoT ecosystem in 2020?

https://www.analyticssteps.com/blogs/how-does-internet-things-sketch-smart-city-under-iot-ecosystem-2020