Hire a team to develop a neural network

Neural networks, or NNs for short, are the most successful format for implementing artificial intelligence, which works on the principle of functioning of conventional biological neural networks.

Simply put, neural networks imitate the work of the human brain using various mathematical models. Because of this, the principle of their operation is fundamentally different from classical software – after all, they do not follow a specific algorithm, and the result of their work often becomes much more variable and even creative.

In reality, the capabilities of neural networks are often underestimated – many still believe that everything is limited to ordinary voice assistants and chat bots. At the same time, big brands such as Apple, Meta and Volkswagen are making the most of NNs, which allows them to conquer global markets and increase their profitability many times over.

Therefore, in this article we will talk about exactly how companies from different fields can use neural networks, what advantages it will give them, and how to select a qualified team to develop, train and implement neural networks in their own business.

Before proceeding to the main topic, we propose to consider exactly how neural networks work with data and what tasks they can solve. Here it is worth highlighting several key points at once:

• Data analysis. NNs are able to analyze large amounts of data and discover hidden dependencies. This can be used, for example, to find the best ways to increase website conversions or to detect fraudulent transactions.
• Forecasting. Using the generalization and correlation of data, neural networks have the ability to predict the future of certain sequences of events based on existing factors.

• Clustering. Here we mean the breakdown of the input data stream, not small fragments, without specifying any features or classes. At the same time, how exactly to fragment information is determined by the neural network independently.
• Approximation. Neural networks are capable of replacing complex input objects or functions with simpler ones, but with similar parameters. An example is the replacement of cumbersome man-made weather forecasting models with the simpler NN MetNet mathematical models from Google.
• Data compression. Neural networks have associative memory – that is, they see patterns between objects and parameters, and exclude the reuse of the same data. This gives them the opportunity to work as compactly as possible.

Understanding the basic principles of how NNs work, we can consider exactly how they can be used in different areas of business:

• E-commerce. Artificial neural networks are able to analyze the traffic in online stores and the previous requests of each user. Based on this data, they form a list of personal recommendations and even develop individual promotions for each customer. Ultimately, this significantly improves conversions and quality of service.
• Retail. In this direction, neural networks help optimize logistics and sales chains, optimize marketing, monitor stock balances and count goods on shelves. One example of using NNs in this direction is the Walmart store.
• Finance and banks. Here, neural networks are predominantly used for security, fraud prevention, and market prediction. For example, solutions from SAS Real Time Decision Manage help analyze risks when issuing loans.

• Video services. Almost all popular content providers such as Netflix, YouTube and TikTok actively use neural networks. In fact, everything we look at on these sites is AI recommendations.
• Automotive industry. Here, neural networks are mainly used to automate the control of mechanisms, and can even act as an autopilot. One of the clearest examples of this is the artificial intelligence in Tesla.
• Healthcare. NNs are able to make accurate diagnoses based on customer complaints and even prescribe the best treatment themselves. One example in this industry is IBM’s Watson Health solution.
• Logistics. Using mathematical models, neural networks are able to independently lay out optimal logistics routes. As a result, companies begin to save a significant part of the cost of fuel and depreciation of the fleet, as well as significantly reduce the time of delivery of goods.

Only a few people or a team of dozens of specialists can participate in the process of programming and training a neural network – it all depends on the amount of work and the planned release date of the product in production. However, in any case, positions in the development team remain unchanged.

Consider what kind of specialists our company can provide for the implementation of such a project:

• Project manager. Manages the team, deadlines, budget and is in constant contact with the client side. The main task of PM is to bring to the market the highest quality product that meets the requirements of the customer.
• Data Scientist. Extracts, aggregates and synthesizes data from a variety of structured and unstructured sources, and develops and applies intelligent learning based on real-world data.

• ML Engineer. The tasks of these specialists are in many ways similar to the Data Scientist, but their work is more “technical”. In fact, ML Engineer experiments and implements various Machine Learning algorithms to achieve the set results and trains neural networks.
• Backend developers. Participate in the development of neural networks quite indirectly. Their area of responsibility is organizing the work of servers, building database architecture and integrating a neural network with software that your company already uses in its work.
• Embedded developers. They can be involved in cases where NN will use hardware devices to collect information. For example, if we are talking about using a neural network in retail chains, where you need to track the availability of goods on the shelves, stock balances in warehouses, storage conditions for products, and so on.
• QA Engineers. The neural network model is tested for errors.

Programming and training neural networks is a complex process that consists of several successive stages. Let’s consider them in more detail:

• Collection of information. First of all, developers define the goal and objectives of the neural network based on the requirements of the client.
• Design. The fundamental stage, during which the composition of the input and output data is determined, the optimal architecture and structure of the neural network is selected, and the optimal stack of technologies that will be used for its technical implementation is selected. In fact, according to the design results, it becomes clear exactly how NN will work and how it will solve the set business tasks.

• Data extraction. At this stage, developers prepare the initial data that will be used to train the neural network.
• Parameter initialization. The next step is to ensure the uniformity of neural network training. To do this, the filter parameters are initialized, in which the mean value of the parameter is equal to zero, and the standard deviation is equal to one.
• Creation of self-learning opportunities. One of the main characteristics of a neural network is the ability to self-learn. For the technical implementation of this issue, special functions are created to backpropagate the error through the pooling and convolutional layers.

• Building a network. Next, the developers create the neural network itself, which can receive input images. process them and output the expected output.
• Neural network training. After completion of development and initialization, the neural network has random weights – that is, they have not yet adjusted to the desired result. Therefore, she needs to be trained. The learning process itself is as follows: different data are given as input, NN analyzes them, after which it is given the correct answer. Thus, the network will “strive” to adjust the weights of the synapses so that the output is correct. This process is carried out in iterations – by repeatedly going through training sets. Moreover, the more iterations are performed, the more accurate the results will be.

• Contrasting. As a result of training, it often turns out that the absolute values of the weights of synapses are close to zero and practically do not affect the quality of the result. However, they waste a large amount of computing resources. To solve this problem, such coefficients are set to zero.
• Testing. Before starting the neural network, you need to make sure that it works correctly. To do this, QA engineers perform testing using test sample data examples.
• Practical use and further training. After the start of operation of the neural network, pairs of input and output values can be used to further fine-tune the weight parameters. Simply put, a neural network can be retrained so that it produces the most accurate results. This is especially important if the initial sample size was relatively small.

Artificial neural networks are able to process large amounts of data, find patterns and independently make decisions to optimize various business processes. This makes them almost indispensable when it comes to improving business efficiency and profitability. Moreover, there are already many successful cases of using this technology in various industries.

At the same time, programming and training neural networks is a complex process that requires developers to have the appropriate knowledge and skills. For the implementation of such a project, AVADA MEDIA can provide already formed teams of experienced professionals who will be able to translate tasks of any complexity into reality.