**Introduction to Deep Learning**

Deep learning is a subset of machine learning that is concerned with algorithms that can learn to represent and model data in multiple layers, often composed of hidden units. These networks can learn to perform complex tasks such as image recognition, natural language processing, and detection of fraud. Deep learning applications have seen a resurgence in popularity due to the advent of big data and the availability of large training datasets.

Deep Learning: What It Is and Why You Should Care introduces you to this exciting field with an overview on how deep neural networks (DNN) work; where we will see them in use next; and why it’s important for business leaders today to understand how these systems function.

In addition, you will find practical advice on how to apply deep learning algorithms to common business challenges. Read on to learn more.

**What Is Deep Learning And Why Should You Care**

Deep learning is a neural network with three or more layers, inspired by the structure and function of the human brain. The algorithms, called artificial neural networks, “learn” from large amounts of data to make predictions and help optimize and refine for accuracy.

Deep learning has seen a resurgence in popularity due to the advent of big data and the availability of large training datasets. In addition, deep learning has been shown to be more effective than traditional machine learning techniques for tasks like image recognition and natural language processing.

So why should you care? Simply put, deep learning is a powerful tool that can be used to solve complex problems. As businesses increasingly rely on big data and artificial intelligence for competitive advantage, it’s important for business leaders to understand how these systems work and the ways they can be applied.

**What Is A Deep Neural Network?**

The neurons in deep neural networks can be organized into three groups: the input layer, which processes the raw data; one or more hidden layers, which use mathematical transformations to generate abstract representations of the input; and the output layer, which translates those abstract representations into whatever outputs are desired.

Let’s take a look at a simple example. Consider the task of recognizing handwritten digits, such as the number 6. A deep neural network for this task would be composed of several layers, each of which is trained to recognize a different feature of the digit. The first layer might be trained to recognize the general shape of the digit, while the second layer might be trained to recognize specific features like the curve at the top of the 6.

As the network receives more and more data, it will learn to associate certain features with certain digits. For example, it might learn that all digits with a curved top are 6’s, and that all digits with a straight top are 9’s.

Let’s consider how we might create a neural network that learns to perform this task. A simplified version of the task involves creating a neural network with just three layers: an input layer, a hidden layer, and an output layer. The input and output layers are both fully connected, meaning that every neuron in the input layer is connected to each neuron in the hidden and output layers.

The first step to training this network is to define what we mean by “correct.” In this case, if we receive the sequence 789 as an input, we know that it’s supposed to be a 9. Therefore, we can use 9 as our target output.

To train, or learn, the network we’ll adjust the weights and biases of each neuron in the hidden and output layers using a technique called backpropagation. We start with an initial guess at the weights and biases for this neural network and pass some sample data through it (in this example, the sequence 789).

Here’s where things get interesting. As the data passes through the network, each neuron adds up its inputs and compares it to our target output (in this case, 9). If a neuron’s total is closer to 9 than we started with, we know that it correctly recognizes a pattern in our input data, and we adjust its weights and biases accordingly. We then repeat this process for every neuron in the hidden layer as well as those in the output layer.

In this way, as we train our network it will recognize patterns in our input sequence and learn to generate a 9 for an input of 789.

Now imagine that instead of a sequence of 789, we receive as input the sequence 879. Because there’s no such thing as a digit with four 1’s and three 9’s, our network will recognize that it doesn’t recognize this pattern error and give us an output other than 9.

By extension, if we pass an unfamiliar sequence through our network, it will give us some output that we can interpret as an “unknown” or “not a digit.”

To summarize, the training process takes in data and a desired output. It then adjusts the weights and biases of every neuron in the network so that they’re slightly more likely to generate the desired output for a given input.

Of course, this example is an extremely simplified version of deep neural networks. In real-world applications they can have many more layers, many more input and output neurons, and significantly higher complexity.

However, this is a good introduction to the building blocks that make up these complicated neural networks as well as an overview of the process by which they learn.

**Why It’s Important For Business Leaders Today **

As machine learning (and, by extension, deep learning) becomes more prevalent in business processes over the next few years, leaders will need to understand how these technologies can affect them.

The first thing that companies should do is make sure they are collecting all relevant data. No matter what business you are in, your company likely has large amounts of data that can be used to identify opportunities for improvement and growth. By investing in advanced infrastructure and analytics skills, companies can turn this data into actionable business insights.

While deep learning has been around for some time, its current resurgence is largely due to three factors: the availability of large amounts of data (“big data”), the availability of Graphics Processing Units (GPUs) that can handle the large amount of computation required for deep learning, and the development of better algorithms and software libraries.

Today’s businesses are awash in data. Machine learning, deep learning, neural networks and AI can be used to draw actionable insights from all of this information.

## Deep Learning Applications in Different Industries

**Finance**

In the finance industry, neural networks and deep learning is being used to develop predictive models for things like stock price movements and credit risk. These models are becoming increasingly accurate as more data is fed into them, and they are likely to play a larger role in financial decision-making in the future.

**Advertising**

Advertising is another industry that is benefiting from deep learning. By analyzing large data sets of user behavior, companies like Criteo are able to identify potential customers with a high degree of accuracy and target them with relevant ads. Because their customers pay them only when they complete a sale, such systems can have significant financial impact on their bottom line.

**Image Recognition**

A deep learning model can help solve problems that are not easily reduced to statistical algorithms, such as image recognition. For example, Facebook’sDeepFace algorithm is 97% accurate at identifying humans in images, despite the fact that it has never been explicitly taught what a face looks like.

In addition to these applications, deep learning is being used in a variety of areas, including robotics and drug discovery.

These are just a few examples of how deep learning can be used to solve practical problems.

**The Limiting Factor For Businesses**

Currently, the limiting factor for many businesses is not an understanding of the technology or networks but lack of available talent – there just aren’t enough people with expertise in machine learning right now to do all the work that needs to be done.

For example, a recent paper from Google showed that replacing a human with an ensemble of neural networks is more accurate than the original person at identifying stop signs in images captured by car-mounted cameras.

To create this system and achieve this result, though, they used five different neural networks to identify each stop sign pixel by pixel, and then combined the results. The paper does not state, but it is likely that no one at Google has expertise in all five of those sub-fields of deep learning!

This is why there has been an explosion in the number of applications for post graduate machine learning courses on Coursera (and other online program sites) – people see this as an important skill in the job market.

However, while there is currently a talent shortage when it comes to deep learning experts, this will likely not be the case in five years or so. A massive number of people are being trained in machine learning right now, and their expertise will eventually start reaching companies worldwide, thus reducing the talent gap significantly over the next few years.

The only way to be successful in today’s marketplace is to know about the technologies that are changing it, and deep learning is one of the most significant forces driving business transformation over the next decade.

**Where We Will See Them In Use Next**

Deep learning is seeing increasing use in a variety of business applications, including finance, advertising, and image recognition.

The next decade will see a surge in the use of deep learning technology in a wide array of both B2B and B2C applications. As more companies invest money in training their employees on this new skill set, more will start using deep learning in their business processes.

The best way for business leaders to get ready for this new wave of technology is to begin acquiring some basic knowledge about it so that they can understand what it can do and how it might impact them personally. As the number of resources on deep learning increases over the next few years, more and more sources of information will become available.

The only way to be prepared for this change is to start learning about deep learning right now.

Deep learning will inevitably transform many industries over the next several years, but most experts agree that transportation will probably experience one of the biggest changes in how it functions.

**Conclusion**

Deep learning is a subset of machine learning that is concerned with algorithms that can learn to represent and model data in multiple layers. These networks are composed of hidden units, which allow them to perform complex tasks such as image recognition or natural language processing. Deep Learning has seen a resurgence in popularity due to the advent of big data and the availability of large training datasets. The only way for business leaders to be prepared for this change is by starting right now!