Frequently Asked Questions 

What is Deep Block AI Bootcamp?

  1. Deep Block AI Boot Camp is a premium AI coding boot camp that enhances the ability to work as an AI developer in 12 weeks even if you are a non-professional or have no prior experience in AI. You may wonder how AI beginners can develop their capabilities to work as AI engineers in 12 weeks. Deep Block's AI boot camp is a coding boot camp consisting of a practical curriculum. Meeting 2 to 3 times a week for 12 weeks, you can experience compressive growth in a short period of time, focusing on how to use AI technology. 
    Current AI engineers will provide you with professional education throughout the curriculum.  Students will develop their ability to write code on their own based on what they have learned, from the initial setup of AI model to construction and implementation of AI-based system. 
    Computer vision and AI system-building specialized curriculum are also the strength of Deep Block Boot Camp. Omnis Labs Inc. is a developer of Deep Block, a computer vision AI analysis tool. We offer students the best training course based on their skills and experience gained from developing various computer vision AI solutions. Through the courses built on the hands-on experience of AI engineers from Seoul National University's computer engineering department, you can acquire various skills from AI theory to computer vision AI service development, and quickly have practical capabilities and knowledge.
  2. A lot of educational companies often provide AI courses that only cover the basic course of our curriculum, and most of them copy the contents of AI courses from famous universities. Omnis Labs Inc. provides hands-on training on how to use AI libraries, and provides innovative hands-on AI education that is different from traditional, theory-driven and far-from-practice education. Our courses will help students develop AI development capabilities in a short period of time and help them develop work capabilities that can be used in actual industry.

What are the strengths of Deep Block AI Bootcamp? 

  1. Innovative, Easy AI Education 
    Today, most AI education courses are almost exactly the same as Stanford University or Coursera's AI teaching style. The problem is that all of these education were developed for undergraduate or graduate students majoring in computer science. In order to understand these theory-oriented university lectures, you need to complete four years of computer science, science and engineering education, and these are very difficult to understand even for those who have completed them. 
    This problem is caused by the fact that the education was designed to train researchers who goes to top graduate schools and conduct research. 
    Many education companies around the world have unconditionally copied and introduced these famous university lectures, and most AI technology courses found in the market are similar, far from practical, and very difficult. 
    Based on its AI platform Deep Block and AI solution development experience, and AI researchers' learning and development experience, Omnis Labs Inc. aims to address these issues through new technology-driven curriculum offerings. 
    To understand deep learning theory, you have to deal with a variety of theories, including complex formulas and algebra, but the actual usage of deep learning techniques are very simple. The goal of this training course is to train engineers who can use AI models through technology usage and coding-oriented training, and through Omnis Labs' self-developed training content, students can grow into engineers who can build and use AI models without understanding complex math or theories.

  2. Usage of GUI-based AI Analysis Tool Deep Block for Conceptual Understanding 
    For fast and easy AI technology training, Deep Block AI Boot Camp utilizes Deep Block, an AI analysis tool developed by Omnis Labs Inc. Deep Block is an image analysis tool that allows students to perform the entire process of learning, reasoning, and so on without coding, allowing them to quickly and visually understand the process of building AI models. 
    In addition, in intermediate courses, students implement this process directly in code. In this course, you can quickly prepare training data and use it to train the AI model by leveraging the auto labeling functionality in Deep Block.

  3. Project and Practice-centered Education based on Real-life Data set
    Most AI courses utilize datasets such as MNIST and CIFAR10. These datasets are very small images that are not really visible to the eye, and they are very simple images that are completely different from high-resolution images in the real world and in practice. 
    Naturally, developing AI models that classify these datasets is nothing like developing AI models that analyze the the real world data.
    Deep Block bootcamp offers hands-on practice and program based on a variety of datasets that we can actually see or utilize, not on toy datasets. It focuses on how to utilize the latest AI models that can handle data, and in the final course, there will be a project to build an AI system that can handle real-world data in real time based on the data you have prepared.

What can I expect after completing the Deep Block AI Bootcamp?

  1. You will have the ability you need as a entry-level AI engineer 
    This course will help students learn all the basic skills to become AI engineers, including GIT usage, Linux basics and terminal usage, HTTP communication and web development, deep learning framework-specific characteristics and usage, deep learning basic theory, computer vision technology trends, and various computer vision models.
  2. You will have knowledge and experience in not only AI theory but also AI system building and design 
    Our curriculum aims at training on how to build real-world services based on AI models, not just AI theory and model building. In the last month, students will learn how to develop AI-based services and the various skills and knowledge they need, and during the final project, students will implement their own AI-based services. Students will build and design a whole AI-based system that includes an AI model rather than building a simple AI model. This experience will help A LOT in practice.

  3. You will have a portfolio that shows your capability 
    The final project is to build services based on your own computer vision AI model. This allows you to learn your own AI model, implement it as a service, and build your own AI system that will make your portfolio unqiue.

Curriculum Related Questions

  1. How many lectures are there?
    12 lectures consists of one course.

  2. Do I have to take the basics course first?
    If you don't have enough knowledge about AI or computer engineering, I recommend you take the basic class first. However, if you know specific parts of the curriculum, we can recommend you the appropriate course if you contact us.

Course Related Questions

  1. I don't know how to code at all. Can I still take the course?
    The basic deep learning course is made for those who know basic Python programming. If you've never coded before, you may have difficulties in taking the deep learning basic course. 
    Therefore, if you have never coded at all, please take the Python Basic Programming course first.

  2. I know how to use Python, but I don't know anything about AI. Can I still take the course? 
    Yes! Definitely. Deep Block's curriculum is developed for non-CS majors who want to learn AI technology but lack knowledge of computer science and mathematics.  
    Of course, everyone has different progress and pace of understanding. However, we have a proven system that will allow you to learn and develop enough capabilities to work in the field according to individual progress, even if you know nothing or know a lot.

Coding Related Questions

  1. What is the difference between AI engineering and AI research?
    The word engineering generally refers to the technology of creating a real product or service. Specifically, AI engineers are responsible for converting implemented AI models into real-world services or systems. This process is usually centered on computer science knowledge or coding rather than mathematical knowledge, and is centered on implementation. 
    On the other hand, research is often used as a comprehensive representation of the work of thesis analysis, research, prototype development, model implementation, etc. AI research is mainly conducted by Ph.D. personnel in graduate schools and large corporations, and they mainly do tasks such as reviewing AI papers, implementing the latest models, and analyzing the performance of the models. 
    Analysis and understanding of AI papers requires considerable mathematical knowledge and experience. These tasks are generally difficult even for people who have their master's in computer science, so this course focuses on fostering AI engineers rather than training AI research personnel. 
    In general, AI research personnel are in demand for Ph.Ds, and AI engineers have a wide range of market demands from undergraduate to Ph.D. levels. This means that non-professionals and AI beginners have the potential to get jobs as AI engineers or perform AI engineering tasks through constant learning and effort. 
    In addition, AI researchers generally lack the ability or experience to transform implemented AI models into real-world services, and the demand for AI engineers is exploding across large corporations and startups. Start-ups are focused on implementing services using an open-source library rather than directly conducting AI research, and large companies also need support from these engineers in the process of actual service implementation. 
    Omnis Labs is confident that through our courses, students will be able to create a successful career with the essential capabilities they need as AI engineers.

  2. What coding environment do you use? 
    Simple code creation can be done using various editors such as Visual Studio Code. However, this course does not utilize Google Colab, Jupiter Notebook, etc. and you will learn or infer models directly from Linux. 
    This course is a practice-oriented course, and since real AI specialized companies do not implement AI systems such as Colab, we will learn how to operate actual GPU servers and develop AI systems using them. When you first use it, there may be some inconvenience, but you should be familiar with this environment when you are working on an actual AI development or for your career development. 
    Deep Block AI boot camp has built courses to make it as easy as possible to adapt to these environments, so you don't have to worry too much.

Career Related Questions

  1. How should I prepare job searching based on my AI skills?
    Based on basic programming, computer science capabilities, and basic AI technology knowledge acquired through this course, you can get a job as a developer who can be put into various tasks including AI development. 
    In small start-ups, one developer often has more than one development task. For example, if a back-end developer can even develop AI technology, the company can reduce the burden of hiring and you can become a developer that the company wants even more. Based on these capabilities, you can lead salary negotiations to an advantage and if you can perform various tasks in the company, you will be treated as key personnel in the company.

  2. Can I use my final project as part of my portfolio?
    Of course. The final project is an open-ended project building your own AI model and system. You can build a unique portfolio that is completely different from the mass-produced portfolio that most coding institutes make. And the knowledge gained during this AI system deployment will be helpful during job interviews in the future.