Introduction to Deep Learning

[This article was first published on Methods – finnstats, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Introduction to deep learning, This is a follow-up to one of our previous posts, which you can read here if you missed it.

Introduction to Deep Learning

Deep learning applications are employed in practically every industry, including health care, internet service, telecommunication service (mobile phone), automobiles, and so on…

Deep learning is used in a variety of applications such as image or video categorization, speech recognition, recommendation systems, and so on.

summarize in r, Data Summarization In R »

Why is Deep Learning gaining popularity?

1) Improvements in computer processing power.

2) Access to a large volume of data

3) Advances in AI and machine learning research

Deep learning is made up of three types of methods: supervised, semi-supervised, and unsupervised.

Deep neural networks are neural networks with more than two layers that process data using particular mathematical modeling in each layer.

In general, these networks attempt to extract feature sets from data automatically, which is why they are most commonly utilized in data types where feature selection is challenging, such as unstructured data.

Images, movies, text, and audio data sets are examples.

Principal component analysis (PCA) in R »

Different types of Deep Neural Networks

Let’s start by looking at various deep neural networks.

1)  Convolutional Neural Networks (CNN)

2)  Recurrent Neural Networks (RNN)

3)  Restricted Boltzmann Machine (RBM)

4)  Deep Belief Networks (DBN)

5)  Autoencoders.

Each of these neural networks will be discussed in-depth in a future post.

Customer Segmentation K Means Cluster »

The post Introduction to Deep Learning appeared first on finnstats.

To leave a comment for the author, please follow the link and comment on their blog: Methods – finnstats. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)