Deep learning is a subset of machine learning which uses deep learning algorithms that help arrive at a positive conclusion based on the input data.

Ever wondered how DeepMind’s AlphaGo easily defeated Lee Sedol, one of the best Go players.

No one saw it coming. It totally seemed impossible, but with the help of deep learning, anything is possible today.

Being a subset of machine learning, it now lies at the heart of multiple innovations seen across industries. From self-driving cars to image processing and natural language processing – it’s already here. Most often people think that artificial neural networks and deep learning are terms often used interchangeably, which is incorrect. Not all neural networks can be called “deep” with multiple hidden layers and not all deep learning architectures can be called neural networks.

However, we will further talk more briefly about neural networks and how they can be used to solve multiple complex problems. Although you will find many neural networks present out there, we will only be talking about the ones that are commonly used in the current industries.

Let’s look at some of the important neural network models in deep learning:

**Deep Belief Network**
Deep Belief Network (DBN), with the help of unsupervised machine learning and probabilities, helps generate output. The DBN is different from other models since each layer is orderly regulated and learns the complete input. The DBN encompasses undirected layers, directed layers, and binary latent variables.

In the DBS network, each of the hidden sub-network layers is visible to the next layer. Therefore, enabling a fast layer-by-layer unsupervised training model making contrastive divergence applicable to each of the sub-network. This gets started with the lowest layer that is visible.

Algorithms that are known as greedy learning algorithms are used to train the DBN. These algorithms incorporate the learning one layer at a time. As a result, a different version of data gets added to each layer. Therefore, every layer will use the output from the previous layer to be placed at its input.

Deep Belief Network is highly applicable in the field of motion capture data, image recognition, and video recognition.

In this article, we will only focus on the Better Optimizing algorithm for Deep Neural Network (DNN). We will call this optimizing algorithm as a Learning algorithm for this article.

The past few decades have witnessed a massive boom in the penetration as well as the power of computation, and amidst this information.

“You do not really understand something unless you can explain it to your grandmother” Not sure where this quote originally came from, it is sometimes kind of half-attributed to Albert Einstein.

Deep Learning Explained in Layman's Terms. In this post, you will get to learn deep learning through a simple explanation (layman terms) and examples.

Deep learning on graphs: successes, challenges, and next steps. TL;DR This is the first in a series of posts where I will discuss the evolution and future trends in the field of deep learning on graphs.