When talking about neural networks in IT, we most often think of artificial neural networks. Why “artificial”? Because they are inspired by real-life human neural networks — the aim of both of them is to successfully recognize patterns and interpret them to better understand the world around.
Artificial neural networks, therefore, allow businesses to speed up multiple processes by allowing a machine to handle routine tasks that normally a human would do. This increases the efficiency of a business, helps in cost optimization, and expands the importance of data in decision-making.
How can neural network developers exactly help your business?
Neural networks power a variety of products that are of media’s top interest. Self-driving cars, voice assistants in the likes of Siri or Alexa, and even very scientific areas like forecasting weather or predicting earthquakes — artificial neural networks are the core of all these solutions.
But neural network development can also be useful for more common businesses. We listed some of the most frequent uses of the technology below.
- Classification of data — splitting it into folders, databases, separate columns in sheets, etc.
- Target marketing — segmenting customers based on specific features (e.g. economic status or location) in order to deliver them tailored messaging.
- Reliable forecasting (e.g. of future sales) based on data from multiple sources.
- Fraud detection by identifying anomalies.
- Facial and object recognition — useful especially for augmented reality.
- Voice recognition.
- Customer service chatbots — chatbots can be the first point of contact with a business for a customer and answer the most common questions so an actual person can solve the more complicated issues more efficiently.
- Product design and layout optimization by analyzing customer interactions with the product.
Could humans themselves achieve all the results that the above automated solutions give? Yes. However, it would require way more time and a great number of people involved in the process. And this means that neural network development is simply the best solution for effective cost optimization while keeping the high quality and reliability of the end-results.
Most important hard skills of neural network developers
Neural network development is conducted by specialized developers familiar with the world of AI and machine learning. It takes them years to truly thrive in the field but they finally end up with skills necessary to deliver commercial neutral network development of the highest quality.
What technical skills are we exactly talking about?
- Theoretical knowledge of relevant concepts
Learning about neural network development most usually starts with getting the grasp of the theory — efficient coding is not possible without understanding the concepts themselves and where they came from. Such theoretical background should cover areas like machine learning, AI theory, deep learning, and other relevant concepts.
- Good grasp of complex mathematics
The worlds of coding and mathematics are strongly linked, and this connection is especially important in neural network development. Your neural network developer should have perfect understanding of academia-level applied maths, including probability and algorithms.
- Knowledge of relevant programming languages
Different programming languages can be used for neural network development. Some of the most popular ones are Java, Python, C, and C#. The choice of the programming language is usually either up to the developer or the company that hires them. Many neural network developers know multiple languages but even they can be preferring one over the others. If you have any preference in regards to the language you want to use for your project, look for devs with previous commercial experience with this specific language.
- Experience with relevant libraries and frameworks
The use of libraries and frameworks significantly speeds up the development process. The choice of these tools depends often on the programming language used, the aim of the project, as well as, sometimes, even the neural network developer’s subjective preferences. Some of the most popular frameworks and libraries used in neural network development are TensorFlow, Caffe, Theano, and Torch.
Soft skills a great neural network engineer should have
Even in such a tech-focused area as neural network development, hard skills are not everything. Additional advantages can be gained by hiring a neural network engineer with a specific set of soft skills that allow them to be more adaptable and eager to cooperate with a team.
What soft skills you should be especially looking for in a neural network developer?
- Attention to detail
When developing an artificial neural network, sometimes even a small mistake in code might cause the end-results to be unreliable. That’s why it’s so important for the neural network engineer to understand the value of bug-free code and always make sure to triple-check everything they worked on.
- Innate curiosity
We learn about new developments in the areas of neural network engineering, artificial intelligence, and alike on nearly a daily basis. Solutions that seemed viable a year ago might be considered outdated if a breakthrough happens. It’s crucial, then, for your neural network engineer to always do their research and keep up-to-date with the recent developments in their field.
- Ability to communicate complex ideas in a simple way
While much of a neural a developer’s work happens in collaboration with other engineers, they sometimes must also attend meetings and presentations with less tech-savvy colleagues. Being able to communicate to them complex ideas behind the project without the dev jargon is crucial to healthy cooperation and creating a bond of understanding between the two sides.
Assessing the experience of a neural network developer
Neural network engineers most often have some academic experience with neural networks and other relevant topics (e.g. machine learning, AI) — it’s just easier to understand these concepts with an experienced professor on hand rather than by yourself, at home. These developers are usually graduates in fields like computer science, statistics, mathematics, physics, data science, or some kind of engineering. Courses that lead them to a career in neural network engineering cover topics like:
- Complex mathematics
- Econometrics
- AI theory
- Machine learning and deep learning
- Robotics
- Automation
- Programming
- Cognitive science
Remember, however, to assess also the commercial experience of your candidates. Try to find specialists who have already worked with companies in your sector and who developed solutions similar to those you want to have developed for your business. For example, if you are looking to develop a neural networks-based fraud detection tool, you should be looking for a cybersecurity expert rather than a person who worked mostly with chatbots development. Of course, as the number of neural network developers on the market is still limited, you should not be too fussy, but some level of technological and industry fit between your business and your candidate is recommended.
Asking neural network developers the right job interview questions
To properly assess the knowledge of your candidate, a technical job interview is needed. It can be conducted in multiple ways such as by giving them a task to do at home, running a face-to-face interview and asking multiple questions related to neural network development, or by combining these methods. As you will be tackling very technical areas, remember that if you are not a neural network development expert yourself, you should ask for help from a professional with experience in the field. This can be another developer who works with neural networks (e.g. a machine learning engineer), a software consultant, or an IT project manager.
To give you an idea of how such an interview might look like, we listed below some of our favorite job interview questions to ask a neural network developer.
- Explain the bias-variance tradeoff.
- When is log loss and when cross-entropy used?
- What is regularization and when would you use it?
- What are ReLU and Sigmoid? Which one would you consider better and why?
- What are convolutions? Why would use them for images instead of FC layers?
- What is batch normalization?
- What is cost function?
- What are the differences between k-nearest neighbor algorithm and k-means clustering?
- How to overcome the Curse of Dimensionality?
- Why is naïve Bayes called “naïve”?
Beyond neural networks
Investing in neural network development is a great way to automate your business and make it more data-driven. Additional benefits can be gained by adding even more data specialists to your team, such as AI experts, machine learning engineers, data scientists, or data analysts.
Ideamotive can help you hire all these people fast. We run a network of top IT talents looking for new job opportunities and connect you only with the experts who truly match your company. Whether you need data experts, a web developer, an agile coach, or an IT project manager, we will find you the right people in no time.
Contact us today and start building great data-powered products.
