With the explosion of data being generated at an unprecedented rate, there are many instances where software can process large amounts of information quickly and effectively. This is particularly true in the field of artificial intelligence (AI), which uses computer programs to perform tasks that mimic how humans think.

For example, AI technology is used to predict disease outcomes, find patterns in masses of data, recognize speech, and even beat human experts at games like chess! All of these applications depend on **powerful computational tools known** as deep learning.

Deep neural networks were first introduced in 1989 by Canadian psychologist Geoffrey Hinton. Since then, they have become one of the most important concepts in modern-day machine learning. These days, you will *almost always see people talking* about “deep learning” when referring to algorithms with this name.

Many companies now use deep learning techniques to **solve complex problems across industries**. For instance, Amazon has reportedly built its own self-*driving car using advanced versions* of this algorithm.

Given their popularity, it makes sense to learn more about them. Fortunately, you don’t need any special expertise or training in mathematics to do so!

In this article we will take a close look at what deep learning is, how it works under the hood, and why it is becoming increasingly popular. We will also explore some easy ways to apply it to your own projects, from natural language processing to image classification.

## Practice working with some of the pre-built models

There are *many great machine learning tools* that do not require you to install any software or purchase additional packages. Some companies create easy-to-use applications or softwares that contain built-in algorithms for their products, and they are free!

Many universities make their research codes available so that anyone can access them at no cost. This is very helpful since most of these programs require expensive subscriptions or licensing fees!

In this article, we will be looking into one such tool – The MATLAB® Machine Learning Software Suite. It is an excellent choice if you are new to ML concepts or experienced but want to test your knowledge more thoroughly.

This suite comes packed with several different AI functions, all under one interface. One of these functions is *called deep neural networks* which use supervised, unsupervised, and semi-supervised training strategies.

Here, we will look into how to work with pretrained (or already trained) DNNs in the matlab software.

## Create your own neural network

A lot of people these **days use deep learning** for all sorts of applications, from making chatbots that can hold conversations or even determine if an image contains information about where you should buy things online or not, to creating software that can recognize objects and textures and perform other tasks.

There are some pre-made networks available, but most of them come with very restrictive licenses, which prevent you from using their technology to make your own netowrks.

That’s why it is such a good idea to learn how to create your own neural networks! You will be able to take any type of data and *apply advanced pattern recognition algorithms* to find patterns and insights hidden within the data.

In this article, we will go over everything you need to know to get started building your own neural networks in Matlab. We will also talk about some easy ways to get yourself a free account on Google Cloud Machine Learning so that you do not have to worry about *paying anything extra* to access the tools.

## Use transfer learning

One of the most important concepts in *deep neural networks* is transfer learning. This is when you use pre-trained models that have been trained for other tasks, and you re-train or update those models to perform new tasks.

A famous example of this was when Google used their own internal data set to train its **first convolutional neural network** (CNN) model for face recognition. They then applied this CNN to detect objects in images of cats – which is what it was originally designed to do!

This method can easily be adapted to address problems in all areas of machine learning. By **using already trained weights** from another task, you can apply them to solve your current problem more efficiently.

There are many ways to implement transfer learning in MATLAB. In this article, we will **discuss two easy ones**: fine tuning and domain adaptation.

## Use convolutional neural networks

One of the most popular architectures for computer vision applications is called a Convolutional Neural Network (CNN). CNNs are built by stacking layers that perform specific tasks, such as detecting objects or finding patterns in data.

The term “Conv” in CNN stands for “convolution.” A layer with **many kernels — small sections** of information -is attached to each other sequentially to form larger pieces of knowledge. Kernels are **often repeated several times** so that they can learn more complex features at different spatial locations.

By having multiple groups of neurons working together, CNNs can achieve excellent results when applied to images and video. Because the network learns how to combine individual parts, it does not require pre-defined feature sets like some **traditional machine learning algorithms** do.

There are two main types of CNN architectures: ones where all layers work directly on raw pixel values and ones where the first few layers transform input into vector representations which later get transformed again. The type you choose depends mostly on what kind of image processing you want to do!

With this article, we will be looking at the second type of architecture, known as VGGNet. This was one of the very *first successful deep learning models used* for object recognition and has inspired newer, better performing architectures.

## Use recurrent neural networks

Recurrent neural networks (RNNs) are an interesting type of network that has become increasingly popular in recent years. RNNs have you as their main user, because you can use them to do almost anything!

They’re called recurrent because information is passed back and forth through the layers of the network over time. This internal memory allows the system to remember past events and apply this **knowledge towards predicting future outcomes**.

For example, if you wanted to predict whether or not someone will *go bankrupt within one year*, then a model using an RNN would look at past financial data to determine how much money they spent, what kind of jobs they had, and so on.

This information would be used to make assumptions about how well these things influence bankruptcy. More specifically, it would factor in average income, number of credit cards, amount of debt, and so on.

These predictions would be weighted according to how strong each variable is and how strongly they affect bankruptcy. Using all of this information, an algorithm could come up with a prediction for whether or not people will run out of money in the next 12 months.

Given enough historical data, this **algorithm could also tell us something** about how likely it is that someone will get into trouble financially in the future. Because we know that those who struggle with debts are more likely to get into big problems later on, looking forward and backward can **help identify potential risk factors**.

## Use k-means clustering

One of the most fundamental tasks in machine learning is cluster analysis or, as it’s sometimes called, unsupervised classification. Cluster analysis seeks natural groups or clusters within datasets.

A classic example of this is understanding how documents are grouped into different categories (like blogs or magazines). More recently, researchers have applied similar concepts to understand patterns in large sets of data — what types of behaviors and activities occur together to make up certain “types” of people or organizations.

Cluster analyses can be very intuitive. For instance, imagine that you wanted to classify all flowers into one of two categories: plants or not. You could look at each flower independently and determine if it was a plant by its leaf structure or *stem growth pattern*, but then you would need many, many instances of both to get accurate results.

Instead, you could take a step back and think about whether there are any rules or guidelines that apply to every plant for determining if it is a plant or not. Most plants seem to follow the same set of criteria: they must have leaves and stems.

By looking at every sample using these general principles, you now have an algorithm that can identify if something is a plant or not! This process is known as K-Means Clustering.

Deep neural networks use a technique like K-Means extensively to achieve their goals.

## Use regression models

A second way to use deep learning tools in matLAB is using it for regression tasks, which are predicting an outcome or value for something. For example, if you wanted to predict how **many points someone would get** on a test then regression can be used to determine that.

Regression uses algorithms like linear regression where your model predicts a number dependent on other numbers. In this case, the algorithm learns what numbers correlate with having more points on a test so those numbers get weighted higher when calculating the predicted score.

Another common regression task is predicting time-dependent values such as how long it will take to complete some work. Algorithms like back propagation of **error apply mathematical concepts** to learn timing predictions.

There are several ways to implement regresssion in MATLAB. You could write your own code or use one of the pre-built functions or libraries that have been designed specifically for regression.

## Use deep learning algorithms

Recent developments in *artificial intelligence revolve around* so-called neural networks. A neural network is an algorithm that works by having several layers of nodes (think: neurons) connected to each other.

The way these layers are interconnected is called the architecture, which determines what the network will learn. For instance, *convolutional architectures typically look* for patterns within images, while recurrent ones learn about sequences.

There are many types of neural networks, but one of the most popular right now is known as a deep neural netowrk or DNN. These have *multiple hidden layers beyond* the input layer, where information is processed.

You can use them to perform tasks such as image recognition or natural language processing. Some **applications even combine different features** into one system!

Here we’ll take a look at how you can implement some basic DNNs in MATLAB. We’ll also see some more advanced uses of this toolbox.