Hire GAN Network Experts And Consultants

Yann LeCun, Director of AI Research at Facebook and an NYU professor, wrote in 2016 that GANs are "the most interesting idea in the last 10 years in machine learning”. But what are GANs actually and why they are so revolutionary?

GANs, or generative adversarial networks, are generative models composed of two opposing (therefore „adversarial” in the name) networks. One of these networks generates new data instances (generator) and the second reviews whether these instances fit the existing data catalog (discriminator). The idea of GAN networks was introduced in a 2014 paper by a group of scholars from the University of Montreal.

What GAN networks can be used for?

GAN networks became popular among the broader public mostly thanks to the developers who used them for creative purposes — and those got quickly picked up by news outlets and websites around the world. 

One of the most popular cases of using GANs was the 2018 story of the Portrait of Edmond Belamy. This work of art is a result of a collaboration of three French students who, with the help of a GAN algorithm built by Stanford’s Robbie Barrat, issued the first AI artwork to be sold at an auction house, the famous Christie’s.

As Hugo Caselles-Dupré, one of the three French students explained:

 We fed the system with a data set of 15,000 portraits painted between the 14th century to the 20th. The Generator makes a new image based on the set, then the Discriminator tries to spot the difference between a human-made image and one created by the Generator. The aim is to fool the Discriminator into thinking that the new images are real-life portraits. Then we have a result.

GANs are also commonly used to generate fake news, especially in form of the infamous deepfakes — most often videos in which a virtual lookalike of a real person says something they have never said in real life.

But GAN networks can be also used for the good of your business.

The business advantage of GAN networks

GAN networks are still a relatively new branch of AI development so new ways of incorporating them into business operations are being discovered on a regular basis. At the moment, GANs are used mainly for:

1. Improving customer service chatbots to better imitate humans.
2. Enhancing cybersecurity measures by identifying weaknesses in the current system.
3. Creating content that sells (e.g. business listings, social media posts, SEO copy) based on the best performing examples from all around the internet.
4. Finding anomalies, irregular activities (e.g. suspicious transactions on bank accounts, unusual results in medical laboratories).
5. Identifying new solutions to problems (e.g. finding new medicines).
6. Generating virtual worlds and assets for video games and movies.
7. Personalizing content presented to customers by identifying and reviewing their preferences.
8. Improving AR (augmented reality) technology, for example in regards to face filters.

In other words, GAN networks are surely useful in any type of industry and have already become an important part of machine learning and AI development world. This means that for the best results in automation and improving your data science division, you need a GAN networks expert.

Crucial hard skills for effective GAN engineering

GAN engineering is most often done by experienced machine learning developers focused on such networks. To excel in the role and deliver great results for your business, GAN engineers should possess a specific set of technical skills — we list the most important ones below.

1. Theoretical knowledge and practical experience with various approaches to machine learning, deep learning, AI development.
2. Previous experience with developing specifically GAN networks.
3. Experience with various operations on data, including data analytics, data engineering, data visualization.
4. Proficiency in relevant technologies. In the case of GAN networks development, the most common choice is Python with Keras (often on top of Tensorflow or PyTorch).
5. Good understanding of complex mathematics, such as probability or algorithms.

Importance of field knowledge in GAN engineering

As there are so many various uses of GAN networks in business, different GAN developers are experienced in different fields of machine learning. For example:

• If you wish to hire GAN network engineers for a fintech, you will most likely profit the most from developers who are focused on cybersecurity.
• If you look to improve your AR software, you need GAN engineering experts who have already worked on facial and object recognition.
• If you wish to improve your marketing or website conversion by delivering perfect content, your best bet is GAN networks experts experienced in working on data extraction and operations on tens or hundreds of thousands of data entries.

As GAN engineering is still a very new discipline, there are not that many experts in the field on the market. Because of this, you should not focus on finding people with experience in your specific industry. Of course, there is, for example, a number of GAN developers working in financial institutions that will be a great choice for fintechs. However, most organizations will benefit the most by looking into the aforementioned field fit — reviewing what your business needs GAN networks for and finding a GAN engineer who worked on similar solutions but not necessarily in the same industry as your business.

Soft skills needed for best results in GAN engineering

When you want to hire GAN network engineers, you should also consider specific soft skills that have a direct influence on the results they deliver. Such skills are:

1. Researcher attitude
   
   Machine learning, and therefore also GAN engineering, is not for people who believe that learning something once is enough to excel. In such an ever-changing environment, being ready to research tons of online forums and academic papers is crucial to innovate and improve the company’s GAN engineering and solutions.
   
   
2. Attention to details
   
   One small error in an algorithm can prevent GAN networks from working properly. GAN engineers should always be careful and work on the project fully concentrated.
   
   
3. Excellent communication skills
   
   GAN engineers often work in teams with other machine learning developers. Being able to communicate well with them is crucial for delivering reliable solutions aligned with the company’s needs. 

Education of GAN networks experts

GAN networks engineers are most often graduates in fields like computers science, maths, statistics, or some kind of engineering. To excel in AI and machine learning development, throughout the years of studying, they most often participate in courses on:

1. Complex maths (probability, statistics, algebra, calculus, logic, algorithms).
2. Probabilistic graphical models, such as neural and Bayesian networks.
3. Physics, engineering, robotics.
4. Cognitive science.
5. Computer science, programming.
6. Data analytics, operations on data. 

Job interview questions for generative adversarial network engineers

A technical interview allows you to assess the full scope of your candidate’s knowledge about GAN networks and other relevant concepts. If you are not a machine learning professional yourself, we highly recommend asking such an expert to assist you in running the interview.

What questions can you expect to be asked?

1. What is a cGAN?
2. What is boosting in machine learning? How can it be achieved in GAN networks?
3. What is naïve Bayes and why is it called “naïve”?
4. When is Ridge regression better than Lasso regression?
5. How can you overcome the Curse of Dimensionality?
6. What is LSTM and what are the key components of LSTM?
7. What is the difference between L1 and L2 regularization?
8. How can F1 score be useful?
9. How to prevent overfitting?
10. How to combat an imbalanced dataset?
    

Who else do you need?

GAN network engineers are an incredibly valuable addition to your company, but improved efficiency and great results can be achieved by expanding your data team even more. Data scientists, data engineers, machine learning engineers, AI experts — they are all useful when it comes to innovating and automating your business.

With Ideamotive, hiring all these experts is easier than ever. We run a network of top IT talents looking for new career opportunities, including a vast pool of data and machine learning professionals. For the best results, we connect you only with candidates who perfectly match your company profile, product specification, and any other requirement you might have.

Get in touch with us today and start innovating your business with a great team.