According to McKinsey's report, deep learning techniques account for approximately 40% of the annual value created by analytics. The investments in the field are getting increasingly popular as a number of companies decide to hire deep learning engineers to help them with automation, forecasting, analytics, and more. In fact, according to a report by Grand View Research, the global deep learning market size will likely grow to more than 10 billion dollars by 2025.
But what is actually deep learning? How can it help your company? And, when you finally decide to invest in deep learning, how to hire a deep learning engineer to help you incorporate the techniques in your business? We answer all these questions in our guide below.
How can deep learning engineers help my business?
The term deep learning is sometimes used interchangeably with the term machine learning. In fact, however, deep learning is a subset of machine learning — one, although especially popular, approach to ML in the sea of others.
What makes deep learning unique, is that it is a method inspired by how human brain functions. Using artificial neural networks, deep learning engineers aim to teach a machine how to recognize patterns and interpret them. This covers, for example, voice recognition, incredibly important in voice assistants like Apple’s Siri or Amazon’s Alexa, and image recognition used in autonomous cars.
Deep learning is also being utilized in science. Back in 2017, Google's deep learning tool used to identify breast cancer cases had an accuracy of nearly 90%.
But deep learning can be implemented for any business niche. Most companies use the technology for:
- Data classification — assigning it to specific folders, databases, columns in a spreadsheet, etc.
- Targeted marketing — segmenting (potential) customers by their specific features (e.g. economic status, age, gender, location) in order to build targeted ads and tailored versions of a product.
- Forecasting (e.g. future sales, machinery failure, the future performance of investments).
- Detecting fraud and cyber intrusions by identifying anomalies (e.g. unusual activity on a customer’s account).
- Improving customer service chatbots.
- Facial and object recognition — mostly used for AR (augmented reality) experiences.
- Optimizing customer recommendation algorithms — commonly used by online marketplaces.
- Improving the safety of the manufacturing process by eliminating human error.
A huge share of advantages coming from using deep learning is, then, linked to letting the machine handle routine tasks. These tasks, if done by a human, would take longer to be accomplished. In the end, then, although first you have to invest in deep learning engineers, using these machine learning techniques will help you optimize costs in the long run.
The most important hard skills for a deep learning developer
Deep learning developers are directly responsible for building, testing, and analyzing artificial neural networks. Although with such an engineer’s help a machine should be able to learn and even find data by itself, the process still must be overseen by an expert. This is done to make sure that the algorithm is continuously working correctly, providing reliable results.
What technical skills should a deep learning engineer have in order to deliver artificial neural networks of top quality?
- Experience with a relevant programming language
Deep learning engineers operate on different programming languages, usually based on their own or their company’s preferences. Deep learning algorithms can be build using Java, Python, C, C#, and some other languages. If you are not sure which one of those is the best choice for your company, you should review your options with a software consultant prior to starting the hiring process for deep learning engineers.
- Experience with relevant libraries and frameworks
Nowadays, whatever type of digital product you are developing, your engineers will surely be using specific libraries and frameworks to speed up the production and work efficiency. Deep learning engineering is no different — there are multiple tools like this available for developers, and most popular ones include TensorFlow, Caffe, Theano, and Torch. The choice of exact libraries and frameworks most often depends on the programming language used and the specific aim of the neural networks developed.
- Understanding of relevant concepts
One cannot become a deep learning engineer with just the coding skills — theoretical knowledge of topics like AI theory or machine learning is also crucial. Without understanding these, creating a reliable neural network is not possible.
- Data modeling and evaluation
Needed for creating clear criteria of what type of data should the neural network use.
- Applied mathematics
Especially probability and statistics — these are the backbone of deep learning engineering.
Crucial soft skills to thrive in deep learning development company
While the above set of technical skills is truly a must for deep learning engineering, your candidate should also possess specific soft skills for ultimate efficiency. Such set of soft skills might include:
- Attention to detail
To secure the artificial neural networks from delivering unreliable results due to bugs or other issues introduced by accident.
- Curiosity
To always research recent developments in deep learning and implement innovative solutions.
- Ability to skip technical jargon
To explain complex ideas to less tech-savvy peers at the company.
- Expert time management
To always find time to not only build new things but also optimize and maintain already released solutions.
The education path for deep learning developers
Becoming a deep learning expert requires tons of professional (including theoretical) knowledge and self-motivation to learn it all. That’s why there are not that many self-taught deep learning engineers — most of them learned about all the relevant concepts at universities. They are most often graduates of majors like computer science, statistics, (applied) mathematics, physics, data science, or some kind of engineering. They build their interest in deep learning, machine learning, AI, and other relevant topics by attending courses on:
- Complex/applied maths
- Econometrics
- AI theory
- Machine learning and deep learning
- Cognitive science
- Programming
- Robotics
- Automation
The importance of experience in deep learning engineering
When you plan to hire a deep learning developer, remember to assess not only their education but also commercial experience. It’s best if they already have worked for a company to develop neural networks and understand the difference between academic research and more focused commercial development with specific business goals to reach.
Look especially for deep learning engineers who have previously worked…
- In companies within your industry — so they have some knowledge of the most relevant data sources and target customers within this industry.
- On solutions similar to yours — so they can deliver results faster and more efficiently. For example, if your deep learning solution will utilize image recognition, choose a dev who already has experience with image recognition neural networks, not a chatbot expert.
Running a deep learning engineer interview
In order to make sure you are indeed hiring an experienced, top-quality deep learning developer, you should run a professional technical interview with them. This can cover a set of questions on their technical knowledge or even a task (such as writing a specific algorithm) to be accomplished by the candidate at home. Of course, such an interview should be run by a deep learning expert — if you are not one yourself, consider asking for help from another machine learning engineer, or a software consultant/IT project manager with an understanding of deep learning.
Below are some of our favorite deep learning engineer job interview questions. Some of them surely will be asked when interviewing your candidate.
- Log loss and cross-entropy — when are they used?
- How to combat the Curse of Dimensionality?
- Why use batch normalization?
- What is F1-score?
- Boosting and bagging — what are the differences?
- How is Bayes theorem useful in machine learning?
- What’s the Fourier Transform? How is it useful in machine learning?
- How would you build an algorithm that suggests a franchise where to open a new branch?
- Why it might be preferable to have fewer predictors in an ML model?
- What is regularization? When would you use it?
Beyond deep learning
Deep learning engineering is a great solution for companies wishing to optimize their business in a way it’s more data-driven and automated. Additional advantages can be gained by investing in the field even more — for example by hiring additional AI experts, machine learning developers, data scientists, or IT project managers experienced in the field of data.
How to hire all the experts, including deep learning engineers, fast? By getting in touch with our consultants at Ideamotive. We run a best-in-class network of top IT talents looking for new career opportunities. To get the best results for your business, we get you connected only with the experts that truly fit your company and the goals you want to achieve.
