Last Updated on June 29, 2020
Deep Learning is the most popular neural network dialect that is used today. It is because it’s a universally useful neural programming dialect and has a straightforward image structure. It also can be seen as a part of other fields like Machine Learning, Artificial Intelligence, and more. However, just a career in Deep Learning is not enough for most of these fields. You will need supporting abilities. For example, if you want to work in probabilistic advancement with Statistics, you will need skills like RNNs, Convolutional networks, Adam, Dropout, LSTM, etc.
Each one of the Deep Learning Specializations – Data Sciences, Machine Learning, Neural Advancement, AI, and so forth need distinctive aptitudes. However, with supported abilities, you can choose to follow the below-mentioned career paths in deep learning:
1. Software Engineer
If a student wants to pursue this path, they must have strong coding skills. For the job, they have to create code supporting the development of algorithms. In simple terms, a software engineer writes a program detailing how a specific function should be performed by a computer. It is a step-by-step instruction manual. Software Engineers have to use the principles of mathematics and computer science for designing and developing software.
If you decide to go for a Deep Learning course, you will be equipped to write programs for different purposes such as network distributions, operating systems, transforming programs into executable files, and much more. Whatever system you create will have to go through rigorous testing. In case there are any bugs, a software engineer will need to examine the code so that they can fix the problem.
A career in deep learning, such as Software Engineering, requires all professionals to hear the needs of the clients and understand them. Then, they have to take this information for building a system as per the parameters of the customers. They are often responsible for the maintenance of the system as well. Software engineers should be proficient in C, C++, Java, etc.
2. Software Developer
In simple terms, a software developer is the one who creates the flow charts that the coders use for doing their job. They are the creative minds working behind the computer programs. In some cases, they might also create the underlying infrastructure that enables the functioning of the computer networks and design specific computer functions.
They are also responsible for making sure that the upgrades work efficiently. Also, they create documentation for the systems for maintenance. Strategic planning is a part of their job as well along with the creation of diagrams and models, plotting how the system will work with its various components. It is the job of a software developer to test machinery.
For this, it is important that the computer functions correctly during the whole testing procedure. The field demands a strong foundation of data structures, computer science, and different computer architecture elements like memory, cache, and distributed processing. Also, since algorithms are so important for the role of a software developer, you will need to study probability and statistics.
3. Designer for human-centered machine learning
The designer who creates human-centered machine learning has to develop systems that can recognize patterns and process information. If you go for a Deep Learning certification program, you will have an in-depth understanding of how a machine can learn.
Programs are manually designed that account for every possible scenario and allow the machine to learn. If this learning is related to human beings, it leads to the creation of a smart user experience. An example of this is Netflix that gives the viewers movie choices of what they might want to watch. Human-centered machine learning is behind the algorithms of Facebook, Instagram, and Twitter feeds as well as YouTube recommendations. Amazon also utilizes this for making product recommendations.
It is also involved with developing software for banks as more and more transactions now are electronically or online. Through complex computer systems, companies can now go through all the financial transfers easily for determining the potentially fraudulent ones. Making these distinctions are important because placing unnecessary fraud blocks on uncompromised accounts causes inconvenience to the customers resulting in reduced purchases.
4. Data Scientist
If you want to become a data scientist, the first and foremost skill you will need is programming. Apart from this, you also must have strong statistical skills. Programming languages known for incorporating statistics easily are Python, R, and SQL. As a Data Scientist, you will also be involved with information analysis – a technique used for discovering useful information by inspecting, cleaning, and modeling data. Through this, data scientists are able to make reasonable conclusions and help the management in decision making.
Data Scientists have to source data located in disparate places for finding actionable insights along with the information on which action has to be taken. The job involves looking for problems and correcting them. The role of a Data Scientist incorporates machine learning as well. You will have to find meaning in the data. They have to understand the implications and human impact on the project and collaborate with other disciplines to get the answers they need.
5. Computational Linguist
The technology of deep learning is often used for voice-recognition software that helps people in navigating through the telephone systems for utility companies, banks, and doctor’s offices. As a Computer Linguist, you will help the computers understand spoken language and improve the systems that exist currently as they make frequent mistakes. An example of this is talk to text applications that are becoming more popular, especially for blind people.
The job of a computational linguist involves helping the computers understand patterns of speech. This way, computers are able to translate words into spoken languages. The aim is to help machines comprehend language. For this, they must be familiar with how human beings use language so that they can reproduce this on the computer. They also must have a strong understanding of syntax, grammar, and spelling of at least one language.
Apart from the above-mentioned roles, you can also become a Data Engineer, Instructor, Research Analyst, Neuroinformatician, Bioinformation, Image Recognition, Research Scientist, Applied Scientist, Research Fellow, Full Stack Web Developer, Lead Manager, and Natural Language Process Engineer. All you need to do is join a Deep Learning course and you will be able to take advantage of all the opportunities the field has to offer.