Artificial intelligence (AI) has become one of the most popular buzzwords across industries, with every company trying to seem like they have some sort of AI system in place. While there is no exact definition for what constitutes AI, many refer to it as “computer systems that are capable of performing complex tasks requiring logic and reasoning”.
With the growing popularity of AI comes an ever-growing number of applications using this technology. One such application is computer vision, which is the study of how computers can recognize patterns and shapes in images or videos. There are several areas within computer vision where machine learning applies: object recognition, facial detection, content analysis, and more.
Computer vision has practical uses beyond just fun games. For example, companies use it to identify products online and automate manufacturing processes. Another area where it is applied is automatic surveillance, where machines analyze video footage to detect suspicious activity. This could be anything from watching for cars driving without license plates to looking out for people breaking into a building.
In this article, you will learn about one particular aspect of computer vision called deep neural networks. These networks apply advanced concepts of artificial neurons to perform image processing. You will also learn some basic terms related to neural networks, like activation functions and layer types. Finally, you will see some examples using Keras, a commonly used Python framework for developing neural network models.
Definition of deep learning
Neural networks are an extremely powerful tool for computer science. Technically speaking, a neural network is any algorithm that has some layers connected to each other. Layer types include input (or perception) layers, batch normalization layers, pooling layers, and dense or classification layers.
The key difference between classic algorithms and neural networks is how these layers connect to one another. A classic example of a layer is an old fashion logarithmic function. The equation would look like this: f(x) = ax + b. So if you were trying to find the value of x such that f(x) equals 5, then you could just plug in 5 and solve for x!
A more advanced way to use logs is to take the exponent of a number. For instance, say we wanted to know what power of 10 corresponds to 100. We could use laws of exponents to do so: e^(log_b(a))=b. In plain English, this means taking the exponential of the natural log of a, which is called raising it to the power of b.
This process can be extended to infinitely many dimensions. Take the examples above and apply them to three dimensional space instead of two. Now your equation will have three variables!
In theory, there is no limit to the complexity of equations that can be solved with neural networks. This makes them very powerful tools for doing image processing and computational imaging.
Examples of deep learning
Recent applications of AI include voice recognition, computer vision, and natural language processing (NLP). Voice commands using Siri or Alexa are increasingly common, where the software uses speech technology to recognize what you say.
Computer vision refers to algorithms that can process large amounts of data to understand images, videos, and shapes. For example, an algorithm could identify objects, determine what types of objects they are, and analyze if something is moving across the screen or not.
NLP involves computers being able to communicate like people do by taking into account tone, body language, and other non-verbal cues. This tech is used for things such as chatbots and virtual assistants like Amazon’s Alexa.
Deep neural networks were one of the most successful techniques used in visual perception tasks such as image classification. As their name suggests, these networks have many layers that each perform simple math operations on input information before passing it onto the next layer.
These layers help the network “learn” more complex patterns and relationships between inputs and outputs. In fact, some architectures use so much repeated layering that they’re sometimes referred to as “deep” nets.
There are several reasons why researchers designed this way, but all agree that deeper networks work better than shallow ones.
How does a neural network work?
Neural networks are very interesting to look at because of how they function. When you use them for computer vision tasks, such as recognizing cats or cars, they create their own internal representation of the image.
This internal representation is referred to as an “embedding” or “feature vector” of the object in the picture. It contains information about the shape, position, and size of the object; sometimes it will also contain context-dependent information, like whether there is water in the picture or not.
The way that these features are combined together is determined by the neural net algorithm used to train the system.
Identify the different layers of a neural network
The first layer of most artificial neural networks is called an input or perception layer. This is where inputs are added to the neural net.
Inputs can be gathered from your surroundings via touch, sight, sound, smell, taste, and other senses. For example, when you look at a picture of someone’s face, what features you perceive depend on your past experiences with similar faces.
By adding these past experiences into the neural network, the computer gets more intelligent about identifying facial features.
The next layer of many neural nets is the process they use to learn how to recognize things. This is known as feature extractionor pattern recognition.
Feature extraction looks for specific patterns within the given information. Once it finds those patterns, the software adds them together to determine the identity of the item being analyzed.
For instance, if you were trying to identify a dog, then looking at some pixels that contain both fur and ears would be a good start.
Identify the different types of neural networks
Neural Networks have become increasingly popular in recent years, with many applications ranging from computer vision to natural language processing. Technically speaking, a neural network is an algorithm that takes input data and processes it into output data by using sequential layers designed to mimic how neurons work in our brains!
There are two major components of most neural networks: fully-connected layers and convolutional (or feature) layers. Fully connected layers take all inputs from each layer before them and produce an output for every possible input, whereas convolutional layers look at small chunks or “windows” of the image and determine what parts of the picture contain important information.
The way these layers connect to each other is determined through what’s called backpropagation. Backpropagation uses the error between predicted values and actual values as feedback to adjust the weights and biases of subsequent layers.
Apply deep learning techniques
Recent developments in artificial intelligence use advanced computer algorithms to perform specific tasks for human users. These systems are trained using large datasets of information, with examples of that content mixed in with the system’s task.
A classic example is taking a picture of an object and teaching the software how to identify what kind of object it is. For instance, if you took a photo of a dog, the system would learn what types of dogs look like and could then describe this new photograph as a puppy or a mastiff.
Similarly, facial recognition technology has become very popular recently. Companies have designed computers to analyze face shape and structure to determine who someone is. This was particularly helpful after many incidents where people wore masks to conceal their identity.
Artificial Intelligence (AI) in general is getting more attention these days due to its potential to improve productivity and quality of life. Technology companies invest heavily in AI because they want to create products that make sense of data automatically.
But there is a fine line between applications that seem artificially intelligent and ones that simply automate simple tasks.
Recent developments in artificial intelligence have brought us deep learning, an advanced computer algorithm that has allowed for significant progress in many areas. Neural networks are built to work like our brains — they learn as they go!
Deep neural networks now do things such as recognize objects, determine speech sounds, manipulate text, and even play games. They achieve this by using what’s called “deep learning.”
What is deep learning?
Deep learning refers to algorithms that use layers of nodes (think neurons) connected together. The more layers there are, the higher up the information goes.
For example, consider playing a game on your phone where you must tap or swipe the screen to move forward. There is only one layer of nodes in that game—the touchscreen sensor, the glass surface, and then whatever program is controlling it.
But once those sensors detect something moving, a second layer recognizes what type of movement it is and determines whether to classify it as being a finger or a paw print or a whisker. And since dogs have lots of hairs, cats hair, and human fingers, there are three possible answers.
That two-layer process is what makes AI so powerful. Games use similar concepts to understand images.
By adding additional layers of perception, computers can analyze larger pieces of data more quickly than just having a few layers.
Identify the different types of neural networks
Neural networks are one of the most powerful machine learning algorithms out there! They have become increasingly popular due to their impressive performance on many computer vision tasks, such as image recognition or natural language processing.
Originally conceived more than 50 years ago, they have regained popularity recently with the advent of so-called deep (or dense) architectures.
Deep architecture refers to structured networks that contain multiple layers of nonlinear transformations connected together in very specific ways. This structure is what enables deep nets to take advantage of the way human brains process information – by breaking down complex concepts into simpler ones through several steps, we get better results.
Deep convolutional neural networks (DCNNs), for instance, use stacks of nontransparent “convolution” filters across layers to extract features from input images. These features are then combined using fully connected layers before being classified according to some internal model.
The success of DCNNs has made them an extremely common tool in AI applications, especially in domains where large datasets can be easily accumulated, such as facial recognition or object classification.