Back in the mid-00s, when machine learning algorithms were at the very beginning of the road towards widespread modern use - it seemed almost surreal to think that one-day complex systems that resemble the structure of the human brain would be anything more than another science-fiction trope.
Now neural network applications are commonplace - the universal tool for all things data analysis and generation - from natural language processing and image recognition to more complex operations like predictive analytics and sentiment analysis.
In this article, we will explain classical Artificial Neural Networks (aka ANN) and look at significant neural network examples.
What are Artificial Neural Networks?
ANN is a deep learning operational framework designed for complex data processing operations. The “neural” part of the term refers to the initial inspiration of the concept - the structure of the human brain. Conceptually, the way ANN operates is indeed reminiscent of the brainwork, albeit in a very purpose-limited form.
The thing is - Neural Network is not some approximation of the human perception that can understand data more efficiently than human - it is much simpler, a specialized tool with algorithms designed to achieve specific results.
The critical component of the artificial neural network is perceptron, an algorithm for pattern recognition. Perceptrons can classify and cluster information according to the specified settings.
Classical neural network applications consist of numerous combinations of perceptrons that together constitute the framework called multi-layer perceptron.
The multilayer perceptron is the original form of artificial neural networks. It is the most commonly used type of NN in the data analytics field. MLP is the earliest realized form of ANN that subsequently evolved into convolutional and recurrent neural nets (more on the differences later).
The primary purpose of the MLP neural network is to create a model that can solve complex computational problems from large sets of data and with multiple variables that are beyond human grasp.
So, what are neural networks good for? The key goals of using MLP in the data processing and analysis operation are:
- Study the data and explore the nuances of its structure;
- Train the model on the representative dataset;
- Predict the possible outcomes based on the available data and known patterns in it.
Now let’s explain the difference between MLP, Recurrent NN, and Convolutional NN.
There are three major types of deep learning artificial neural networks currently in use.
- Classical Neural Networks aka multilayer perceptron - the one that processes input through a hidden layer with the specific model;
- Recurrent NN - got the repetitive loop in the hidden layer that allows it to “remember” the state of the previous neuron and thus perceive data sequences;
- Convolutional NN - contains multiple layers of processing different aspects of data input.
The main difference between them is the purpose of the application. The thing is - the choice of the solution depends on the needs of the operation.
When to use different types of neural networks:
- Multilayer perceptron classical neural networks are used for basic operations like data visualization, data compression, and encryption. It is more of a practical swiss army knife tool to do the dirty work.
- If your business needs to perform high-quality complex image recognition - you need CNN.
- If you need predictive analytics and statistical analysis - it is the job of RNN.
Basic multilayer perceptron consists of at least three nodes arranged in three functional layers:
- Input layer - where information comes in;
- Hidden layer - the one where all the action is;
- Output layer - the results of the operation;
The hidden layer and output layer uses a non-linear activation function that models the behavior of the neurons by combining input with the weights of the neurons and adding bias. In other words - it is a mapping of the weighted inputs to the output.
The learning algorithm for perceptrons is backpropagation - continuous adjustment of the weights of the connections after each bout of processing. The adjustment is based on the error in the output. In other words, the system is learning from mistakes. The process continues until the cost of the error is at the lowest as possible.
There are two types of backpropagation
- Forward pass - where output correlating to the given input is evaluated
- Backing pass - where partial derivatives of the cost function (with different parameters) are propagated back through the network.
In the days of virtually unlimited disc storage and cloud computing the whole concept of data compression seems very odd - why bother? Your company can upload data without such compromises.
This attitude comes from the misconception of the term “compression” - it is not actually “making data smaller” but restructuring data while retaining its original shape and thus making more efficient use of operational resources. The purpose of data compression is to make data more accessible in a specific context or medium where the full-scale presentation of data is not required or unnecessary.
To do that, neural networks for pattern recognition are applied. The file’s structure and content are analyzed and assessed. Subsequently, it is transformed to fit specific requirements.
Data compression came out of necessity to shorten the time of transferring information from one place to another. In plain terms - smaller things get to the destination faster. Because the internet is not transmitting the data instantly and sometimes, that’s a major requirement.
There are two types of compression:
- Lossy - inexact approximations and partial data discarding to represent the content.
- Lossless - when the file is compressed in a way, that the exact representation of the original file.
These days, social media and streaming services are using data compression the most prominently. It includes all forms of media - sound, image, video. Let’s look at them one by one:
- Instagram is a mobile-first application. This means, its image encoding is specifically designed for the most effective presentation on the mobile screen. This approach allows Instagram to perform lossy compression of an image content so that the load time and resource use would be as little and possible. Instagram’s video encoding algorithm is similarly designed for mobile-first and thus applies the lossy method.
- Facebook’s approach can’t be any different. Since Facebook’s users are spread equally over mobile and desktop platforms - Facebook is using different types of compression for every presentation. In the case of images, this means that each image is present in several variations specific to the context - Lossless compression is used for full image screening, while lossy compression and the partial cutoff is used in the newsfeed images. The same goes for the video. However, in this case, users can customize the quality of streaming on their own.
- Despite all its faults, Tumblr contains some of the most progressive data compression algorithms in the social media industry. Similar to Facebook, Tumblr’s data compression system adapts to the platform on which the application is running. However, Tumblr is using solely lossless compression for the media content regardless of whether it is mobile or desktop.
- Youtube is an interesting beast in terms of data compression. Back in the day, the streaming platform used a custom compressing algorithm on all uploaded videos. The quality was so-so, and so in late 00s-early 10s, Youtube implemented streaming encoding. Instead of playing already compressed video - the system is adapting the quality with lossy compression of the video on the go according to the set preferences.
- Spotify’s sound compression algorithm is based on Ogg Vorbis (which was initially developed as a leaner and more optimized alternative for MP3). One of the benefits of Ogg file compression is extended metadata that simplifies the tagging system and consequently eases the search and discovery of the content. Spotify’s top priority is convenient playback.
- Netflix is at the forefront of streaming video compression. Just like Spotify, Netflix aims at a consistent experience and smooth playback. Because of that, their algorithm is more closely tied with the user. There is the initial setting of image quality, and then there is the quality of connection that regulates the compression methodology. By 2020 they are going to adopt the new standard - Versatile Video Coding (VVC) which expands its feature to 360 video and virtual reality environments.
Data encryption is a variation of data compression. The difference is that while data compression is designed to retain the original shape of data, encryption is doing the opposite - it conceals the content of data and makes it incomprehensible in the encoded form.
Multilayer perceptron neural networks are commonly used by different organizations to encode databases, points of entry, monitor access data, and routinely check the consistency of the database security.
These days, encryption is one of the major requirements for the majority of products and services that operate with sensitive user data. In addition to that, in 2018, the European Union had adopted the GDPR doctrine that imposes encryption and data loss prevention software as an absolute must upon dealing with personal data.
Overall, there are three informal categories for sensitive information:
- Personal information (name, biometric details, email, etc.)
- Data transmissions within a platform (for example, chat messages and related media content)
- Log information (IP-address, email, password, settings, etc.)
Today, the most prominent software applications of this category are BitLocker, LastPass password manager, and DiskCryptor.
Presenting data in an accessible form is as important as understanding the insights behind it. Because of that, data visualization is one of the most viable tools in depicting the state of things and explaining complex data in simple terms.
This looks like a job for multilayer perceptron.
Data Visualization is a case of classification, clustering, and dimensionality reduction machine learning algorithms.
Since data is already processed - the major algorithm at play here is dimensionality reduction. Neural networks for classification and clustering are used to analyze the information that needs to be visualized. They identify and prioritize the data that is subsequently processed through a dimensionality reduction algorithms. The data is smoothened into a more accessible form.
Visualization is a transformation of data from one form to another while retaining its content. Think about it as a translation of a notation sheet into a MIDI file.
These days, the most commonly used library of visualizations is D3 (aka Data-driven documents). It is a multi-purpose library that can visualize streaming data, interpret documents through graphs and charts, and also simplify the data analysis by reiterating data into a more accessible form.
Drones of all forms are slowly, but surely establishing themselves are viable multi-purpose tools. After all, if you can train a robotic assembly line to construct cars with laser-focused precision - why can’t try to teach artificial intelligence to drive it.
The groundwork of the autonomous driving framework consists of multilayer perceptrons that connect the eyes of the system (aka video feed) and the vehicular component (aka steering wheel).
The basic operation behind autonomous driving looks like this:
- The algorithm is trained on the data generated by the human driver (usually, it is a combination of vehicle log, stats, and video feed). There are both supervised and unsupervised machine learning algorithms at work.
- The driving session usually starts with planning the route on the map with the location of the vehicle and the place of the destination.
- The route is the approximate plan of the movement. It is adjusted on the go through the input video feed.
- Video feed covers the entire view around the car - from sharp left to sharp right and also on the side and the back.
The video feed is used to:
- Detect objects in the way of the vehicle and nearby;
- Predict the object’s direction to avoid a collision;
- Adjust the direction of movement towards the goal;
Tesla self-driving vehicles use this type of deep neural networks for object detection and autonomous driving. As a service, self-driving cars are live tested in a taxi business by Uber.
Download Free E-book with DevOps Checklist
Customer engagement is a high priority for any company that is interested in a continuous and consistent relationship with their customers. The key is in the value proposition design that is relevant to the target segments and appropriate calls to action that motivate customers to proceed.
The question is how to determine which users go to which category to adjust the value proposition and present an appropriate call to action.
Enter multi-layer perceptron.
The benefits of using neural networks for customer ranking are apparent. Given the fact that every service with an active user base generates a lot of data - there is enough information that can characterize the user. This factor can be beneficial to business operations.
The primary function of MLP is to classify and cluster information with multiple factors taken into consideration. These features are precisely what you need for user profiling.
Here’s how it works:
- User data is processed and analyzed for such metrics as session time, actions/conversions, form filling, signing in, and so on.
- The sum of metrics determines what kind of user it is.
- Then comes clustering. The clusters may be predetermined (with clearly defined thresholds) or organic (based on data itself)
- The results of the calculation from each user profile are compiled and clustered by similarity.
This approach is an efficient and simple way of figuring out what messages to transmit to specific subcategories of the target audience. In addition to being a time-saving and cost-effective measure, it also provides a ton of insights regarding the use of the service or product. In the long run, this information contributes to the improvement of the service.
These days, such algorithms are used by business CRM platforms like Salesforce and Hubspot and also partially by analytics tools like Google Analytics.
One of the many neural network advantages is that it gives us more solid grounds for decision-making and makes us capable of foreseeing different possibilities from the data point of view.
In one way or another, the application of neural networks in various fields gives us a better understanding of how things are organized and the way they function. MultiLayer Perceptrons presents a simple and effective way of extracting value out of information.
Want to receive reading suggestions once a month?
Subscribe to our newsletters