Artificial intelligence (AI) has become one of the biggest buzzwords across industries. Technically speaking, AI is defined as “the field that studies how to make intelligent machines-or computers that apply logic to learn new tasks”.
However, most people refer to technology like chat bots or voice assistants such as Alexa, Siri, and Cortana as examples of AI. These applications are not necessarily considered advanced forms of computer science, but they do use some components of it!
Deep learning is one such technique that garnered significant attention over the past few years. Why? Because it works! And I will tell you why it works in this article.
Many companies have leveraged deep neural networks to solve complex problems in various domains including image recognition, natural language processing, and computational physics. This article will talk about the fundamentals of what makes up a DNN, as well as several uses for these networks.
At the end of this article, you will know everything there is to know about using DLNs in your business. So get ready to start applying them immediately! Here we go…
What is Deep Neural Networking?
Before we dive into some practical uses for deep learning, let us first take a moment to define what a deep neural network actually is. A DNN is just a type of artificial neuron network with more layers than our traditional feedforward net.
Deep learning applied to science
Recent applications of deep neural networks involve using it for scientific purposes or studying patterns in large datasets that can be used to make predictions. For example, by looking at lots of pictures of plants, machines can identify plant species!
A common analogy is comparing humans to computers. Just as we have evolved over millions of years to understand how nature works, computer programs are now teaching themselves about the world through repeated experimentation. This technology, called artificial intelligence (AI) or machine learning, is rapidly growing.
Scientists use AI tools to analyze big data sets containing billions of numbers and statistics. By repeating the process many times, the software learns what makes different types of data similar or different. Computers then apply this knowledge to new data to produce insights and conclusions.
Deep learning applies advanced concepts from neuroscience to these systems. A few layers of neurons connect together to form larger groups, much like groups of cells within our brains connect into more complex structures. Scientists refer to these bigger units as “networks” because they resemble the network of nerves connecting organs in your body.
By applying neuron-like connections between multiple layers of nodes, you get deeper networks that learn more powerful lessons with each passing day.
Deep learning applied to the stock market
Over the past few years, artificial intelligence (AI) has exploded in popularity. Technically sound AIs have been trained using deep neural networks (DNNs) to perform tasks for which they are programmed. These tasks can include identifying objects in pictures or videos, speech recognition, and even playing games!
Many people are now talking about AI having “general purpose” capabilities- systems that learn how to accomplish any task given sufficient training data and computational power.
This seems like a very appealing idea since we want our technology to do more than what it was designed to do. After all, wouldn’t you rather have an appliance that does your basic cleaning as efficiently as possible instead of one that only works with specific brands of vacuum cleaners?
Generalizing beyond its initial domain requires lots of data though, so most experts agree that this will take some time before it happens. Luckily, there are several applications where DNNs are already being used effectively.
In this article, we’ll talk about one such application – the stock market. By applying DNNs to financial datasets, professional traders have made significant progress in their fields.
Deep learning applied to movies
Recent developments in computer vision apply deep neural networks to analyze large amounts of video data to find patterns, predict future behavior, and create applications such as automated movie reviews or predictive policing. Technology companies have designed software packages that use this approach, which is how most of us experience media today!
Deep neural network algorithms are built upon layers of basic computational units called neurons. These neurons connect with other neurons using mathematical functions (or “activation rules”), creating complex relationships that can be leveraged for understanding input data.
The neurons and activation rules used to construct a DNN learn specific information within their parameters, much like humans do. This allows DNNs to recognize subtle changes and pattern matching that people take for granted.
Because they work by looking at repeated examples of what you want them to understand, DNNs can quickly get “trained” on new content, even without any initial settings. This makes them particularly well-suited to tasks where there are lots of training examples, such as recognizing objects or speech.
Research has also shown that DNNs can sometimes outperform traditional machine learning techniques, making it less likely that overfitting will occur. Because these systems develop their own internal models of the world, they are often more accurate than applying someone else’s model to the same problem space.
That said, there are times when simpler approaches perform better.
Deep learning applied to music
Recent applications of deep neural networks (DNNs) for audio include auditory speech recognition, source separation, song identification, instrument classification, and genre detection. Due to its success with acoustic signals, many researchers have shifted their focus onto using DNNs as feature extractors for other tasks such as speaker verification or identity confirmation.
Deep neural networks are inspired by how humans process information. A key element of human perception is that we develop internal models of our surrounding environment which help us understand what we hear. For example, when listening to someone speak, we automatically make assumptions about their gender, age, and body language before adding additional context that may contradict those initial guesses.
In some cases, these assumptions can be incorrect due to cultural differences or ambiguous statements made. By incorporating automatic pattern matching into computer algorithms, however, it is possible to determine if these discrepancies are too severe to ignore. More advanced systems use this knowledge to verify identities or assess risk.
There are several companies offering software solutions that utilize DNN technology for this purpose. Systems like Amazon’s Rekognition and IBM’s Watson offer face matching, voice comparison, and other potential indicators of fraud. While they do not yet rival human level accuracy across all scenarios, they are getting better very quickly!
While there are no hard rules concerning how much data must be used to train the system, the more examples available, the more robust the model will become.
Deep learning applied to advertisements
Recent developments in computer technology have led to another revolution: deep neural networks. These are algorithms that can learn complex patterns, or “layers” of information, from data. Technology companies use these systems to find patterns in large amounts of data to determine what features matter for advertising online ads or finding new products at stores.
Deep learning has already had major impacts in our daily lives, from helping automate self-driving cars to making it possible to diagnose disease using blood samples. It is now being used to create more targeted advertisements, improved smartphone cameras, and even replacing some human workers in certain industries.
There are several types of applications of this algorithm, but they all share one thing: computers that learned how to process information without any kind of manual input. This is different than traditional programs where you give them explicit instructions on what to do next.
Researchers are putting deeper layers into machine learning software to see how well it works and if there are better ways to apply the technology. By studying how past instances of the system operated, we get clues about how to make it work for future problems.
Deep learning applied to kids
Recent developments of deep neural networks have allowed for applications beyond just making pretty pictures or playing games. Companies are using these advanced algorithms to perform tasks that go far more in-depth than what traditional computer software can accomplish. These applications are typically referred to as “deep learning” because of the way they use layers to teach the system how to complete a task.
Deep learning is used in many areas, some of which include speech recognition, image classification, and natural language processing. Speech recognition allows computers to listen to you speak and determine what word you say! This has potential uses in things like automated phone systems and interactive voice response (IVR) services.
Image classification uses features of an image to identify what category it belongs to. For instance, if someone uploads a picture of their dog, a computer could look at the shape of the body, the color of the fur, and so forth to classify the animal. This knowledge can be saved and replayed with other similar images to tell the program what type of animal this is.
Natural language processing uses pre-existing information about words to determine meaning. For example, if there was no context for a word, a dictionary would not know what the word means. A computer can learn the true definition of a word by looking at how others have used it before.
How to use deep learning
So what can we do with this crazy technology?
Well, for one thing, you can use it to find patterns in large datasets so that you can build intelligent systems to perform tasks for us!
We’ve already seen an example of this in our article about how AI is changing the workplace. Now let’s look at some other ways that AI can be used.
You can learn something about medicine by analyzing medical data sets. Or you could teach your phone or computer to recognize objects using pictures and machine learning.
In fact, there are many applications where artificial intelligence has been effectively applied.
If you’re interested in exploring more applications of deep learning, you can start here. You will also want to read our article about the basics of neural networks which you can find here.
Examples of deep learning
Recent applications for deep neural networks include activity recognition for smartphone apps, computer vision tasks such as object classification and detection, natural language processing (NLP), voice modulation identification, and more.
Deep neural network architectures that have been adapted to perform these various computational tasks typically begin with features or patterns extracted from raw data sets via pre-processing steps. These processes usually involve mathematical transformations or manipulations of the input materials to produce output material that can be used later.
The outputs of this process are then fed into larger NNs which learn complex correlations and rules about the input material using backpropagation.