Neural network developers are one of the most in-demand professionals in the informational technology industry. Millions of employers search for talent and offer large salaries, yet since there is a lack of specialists in IT, hiring a good professional is not easy. This article will explain what skills and knowledge must neural network developers have and where to find them.
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What is a neural network?
Neural networks (NNs), also known as artificial neural networks (ANNs), are the most successful implementation of artificial intelligence that employs the biological neural network organization and functioning principles. Hence, they imitate the human brain. NNs use mathematical models that replicate the functions, structure, and connection of biological neurons and neural networks; thus, the computer can learn and make conclusions like a human.
What can neural networks do?
- Clustering. It means partitioning the input data into a set of fragments/classes without first specifying the features and number of these classes. The neural network determines the best way to split data and complete the assigned task.
- Forecasting. By generalizing and identifying hidden dependencies (correlations, relationships) between input and output data, a neural network can predict the future value of a particular sequence based on several previous values and existing factors.
- Approximation. Neural networks can substitute some objects or functions with other more straightforward yet similar (it is called approximation). The clearest example covers complex human-created mathematical models for weather forecasts with simpler mathematical models like NN MetNet by Google.
- Data compressing. Neural networks can find correlations between various data (objects, factors, or parameters) and express the data more compactly.
- Data analysis. Neural networks perfectly analyze multiple data and search for hidden patterns. For instance, it can find the best way to increase marketplace conversion or identify fraud in bank card operations.
How does a neural network function?
A typical artificial neural network architecture has input, output, and hidden layers. Each layer has several computing blocks (neurons) that accept data, process it, do relatively simple computing, and pass it further. What computing a neural network does depends on its type and purpose.
The most common neural network architecture has single input layers (green), two hidden layers (blue), and one output layer (gray).
The input layer accepts the data and splits it into several formats (categories); for example, if the artificial neural network works with processing images, the categories will include brightness, contrast, color, lines, and other image characteristics. Next, the fragmented data go to hidden layers that compute and search for patterns or correlations. The output layer summarizes the calculations and provides an answer as a forecast, action, or conclusion. If the answer is true or above the ‘truth’ threshold, the network enhances the neuron chain that provided it and vice versa.
Practically, it will work the following way. Supposedly you want to make an application that can name the animals on a photo. In this case, the neural network will split the image (input data) into more minor elements on the input layer. Next, the hidden layers will process the fragments, search for the characteristics of a particular animal, and provide an answer (name the animal on the photo) on the output layer.
Teaching artificial neural networks for this purpose will require many images with and without animals in different variations, locations, backgrounds, appearances, etc.
The algorithm of teaching a neural network. Source
NN functioning scheme for searching animals on a photo. Source
The industries of neural network employment
eCommerce. Neural networks are most widely standard in online shops that analyze our former queries and personal data to provide recommendations, products, or even special personalized sales (a popular means in mobile and video games). Furthermore, NN also optimizes publications, making images and 3D models for products. Chat or audio bots on neural networks facilitate communication with clients.
PixelDTGAN neural network makes clothing images that models wear and creates collages of clothing for showcases.
Online cinemas and streaming. Content suppliers like Spotify, Netflix, YouTube, and TikTok) are the other industry frequently use neural networks. Almost everything we watch or listen to on these platforms is the content AI recommends. Furthermore, these platforms also use neural networks to improve the quality of content. If your internet connection is poor, the system automatically adjusts the video or audio quality according to your current internet connection speed.
Retail sales. The neural networks optimize sales chains and logistic channels, fight fraud and thefts, count the products on shelves in shops and warehouses, enhance marketing, and many others. Smart Walmart store in Levittown, New York, where AI on a neural network tracks the supply of products and due date, is an excellent example. Furthermore, Artificial intelligence by Walmart supervises sellers and clients to prevent thefts, fraud, and other violations.
Finances and banking. This industry uses neural networks to analyze, forecast, and fight fraud. SAS Real-Time Decision Manage software helps banks decide whether to issue a loan. NN Finprophet forecasts market direction for fiat, crypto, and stocks. Citibank developed artificial intelligence for preventing fraudulence with bank cards. JPMorgan Chase uses neural networks to optimize document flow, analyze markets and follow KYC/AIM regulations.
Automotive industry. Artificial neural networks optimize and automatize processes, from creating new autos to managing the mechanisms that make cars. Moreover, NNs in the automotive sector also are autopilots; for instance, AI by Tesla. These solutions are not perfect, yet shortly NNs will drive all cars since it is cheaper and more secure.
Health care. The most popular NN solution in the health care industry is IBM Watson Health AI, which diagnoses the patient's complaints and medical history and forms the best treatment regimen. This AI is not the best IBM project yet proves that neural networks can do much for medicine. The article ‘Neural networks in health care industry’ gives an explicit opinion on this topic.
Insurance. Insurance companies often use neural networks to forecast the loss coefficient, reveal fraud schemes, adjust bonuses, etc. NN Allstat analyses data about each driver and offers personalized insurance rates depending on their inclination to accidents.
Logistics. Neural networks cover almost all processes here, from building optimal delivery routes to managing drones and assigning people for work positions based on their skills and experience. Amazon and ETA Windward Maritime have excellent platforms that employ AI by Fourkite, and AI on neural networks do almost all job.
Tesla autopilot sight that works on neural networks.Source
Smart assistants. NNs help with communication through voice and chatbots. You might have interacted with Siri, Google Assistant, or Cortana if you use Apple, Google, or Microsoft products.
ETA Windward Maritime AI™ provides shippers, carriers, and consignees with the most accurate estimated arrival time for maritime shipments. Source
The features of neural network development
Data. The essential element for neural network development is the need for massive data to teach the network. For instance, if you want to create a neural network that will identify people on photos, you will need hundreds of thousands, if not millions, of images with people. Suppose you want to make an application that will forecast the financial markets. In that case, you will need historical data or integration with an aggregator that collects information on these financial markets in real-time.
Collecting information for images and financial markets is relatively easy, while collecting data for other instruments will be hardly possible or complex. For example, you are unlikely to collect much data on accidents and even less on explosions of massive stars in our galaxy. In such cases, you will either have to use synthetic data to train NN or give up on NN.
The process of developing neural networks. Creating and teaching artificial neural networks is a complicated process that requires a lot of time and investment. However, some libraries (NeuroLab, ffnet, SciPy, TensorFlow, Scikit-Neural Network) facilitate the process. Yet, they are applicable only for a limited number of scenarios and often will not suffice for creating unique solutions that focus on a particular business.
The cost of developing a neural network. Developing a neural network is time-consuming, complex, and hence expensive. For instance, the salary of your senior engineer that develops neural networks will reach 135 000 dollars per year in the USA 100 000 dollars in Great Britain or European Union. For comparison, a regular software developer makes 87 000 and 77 000 dollars yearly. If you hire a neural network developer in Easter Europe, for example, in Ukraine, then the salary of a decent professional will start at $30000 a year.
The standard algorithm for building neural networks.Source
According to Indeed data
The technical expertise of a developer. Creating and teaching NNs requires the developers to have specific knowledge of artificial neural networks, machine learning, and artificial intelligence. However, the developers must be excellent programmers and know programming languages and other development instruments. Here is the approximate stack of technologies that you can use to estimate the technical expertise of NNs developers.
How to hire a neural network developer
Here is a straightforward algorithm that will help you find the best neural network developer to launch your project regardless of the level of complexity and specification.
Step 1: Long-listing.
The task: make a list of 20-50 companies that you could select for developing a neural network model.
The process: scan Facebook, LinkedIn, and Clutch.co, Goodfirm.co, Upwork, Toptal, and other platforms for companies that work with neural network development. You may ask your colleagues or partners whether they know of such a company. See who has developed the NN for your competitors. You must focus on finding a company that specializes in your industry instead of just good developers. For instance, if you plan to develop a cryptocurrency wallet or exchange, you will need an experienced technical partner in cryptocurrency and blockchain, like Merehead.
Step 2: Communicate with your long-listed companies.
Task: reduce the long list to 5-10 companies.
The process: contact your long-listed candidates via telephone, messenger, or email. Usually, the response via messenger will come in an hour, while email takes a day. If the wait time is longer, it typically indicates poor communication with customers, creating many problems in the neural network development process.
During the first conversation, you will be asked about your project: what you require the neural network for, your business goals and objectives, the target audience, and so on. Next, the developer should say whether they can cover your need and confirm it with similar projects in their portfolio and the high expertise of their development team. Based on this conversation, you can weed out candidates who don't fit your requirements.
Here are some tips to help you:
You will have to wait two to three days to receive an offer from the companies you’ve short-listed.
- If the response time is long, and you receive the answer several days after the first call, email, or apply, it shows that the candidate has difficulties processing clients’ queries. Not only that, but it indicates that you will have the same issues during the development, so it’s better to avoid them.
- Many clients are new to creating neural network models. Therefore, they will not plan the functionality of the website or application from the onboarding screen to the payment screen. The developers must ask leading questions and explain the website or application mechanics.
- The candidate is more interested in the budget than the task. If the candidate is more interested in your budget during the first contact than your needs, it indicates low interest. Such companies are unlikely to create a quality NN solution.
- The candidate hurries up the events. Developers who want to send you an offer as quickly as possible and conclude a contract should also be avoided.
Step 3: Study the proposals.