Doing research in any field requires you to invest time in learning how to do it properly. For professionals in technology, this is especially true for advanced technologies such as deep learning.
Deep neural networks are computer programs that employ multiple layers of neurons to teach computers tasks. Using lots of parameters (weights) and large amounts of data, these systems can achieve impressive results when applied to the right task.
There are many ways to learn about deep learning, but one of the most effective strategies is to read past papers published by other researchers. This article will help you start this process!
Understand The Basics
Before diving into specific applications or areas of research, you must first establish some fundamental concepts related to deep learning.
These concepts include what types of neural network architectures there are, what activation functions they use, and why minimizing error may not be the best way to train your model.
Create a research topic
Creating your own research project is the next step in doing deep learning research. You can choose any area of machine learning that you are interested in, or even create an entirely new field!
There’s no wrong way to start exploring different areas of ML. By starting with something familiar, you will save some time going through the process of getting trained in the method.
For example, if you already have some experience using neural networks as base architectures for classifying images, then you could look into how they work and apply them to other tasks such as natural language processing (NLP).
By looking at it from this perspective, NLP becomes image classification using words instead of pictures!
That sounds pretty cool, right?
If so, begin brainstorming ideas for applications of NLPs and see what kind of results you can get.
Identify a discipline
Doing research in deep learning means identifying a specific area of computer science or engineering that you are passionate about, and diving into its concepts.
Deep neural networks were inspired by how humans process information – by looking at lots of examples, and then applying what you have learned to new situations.
By thinking about how people learn, computational scientists designed algorithms that mimic this process.
There are many different branches in deep learning, so choosing which one to focus on depends on your goals and strengths.
Computer vision is a vast field that includes things like object recognition and image classification. Speech processing looks for patterns in human speech and applies those patterns to make natural conversations possible for individuals with no formal education.
NLP (natural language processing) uses computers to analyze text and apply rules to determine meaning and content. Applications include automated writing and answering of questions via chat programs such as Slack!
This list is not meant to be comprehensive but rather an inspiration to get started. Check out some of these areas to see if they appeal to you and read through their material to see whether it is relevant to your passion.
Find a lab
Finding an appropriate research setting is one of the most important things you will do as a beginner doing AI research. This article has some tips for you!
Most universities have at least one machine learning or deep learning group that does research in the area. These groups usually have access to GPUs, which are very helpful when it comes to training neural networks.
Many academic departments also have review sessions or seminars where members of the department talk about their work and what they’re planning to do next. Attend these events, and speak with people there so you know who might be willing to help you get started with this field.
We can’t emphasize enough how valuable it is to attend local conferences if possible. Most large cities have at least one major conference per year, and many smaller towns have several per year as well. Not only will you meet other researchers in your field, but you’ll also learn more about the community as a whole.
Write a proposal
Doing research in deep learning is not easy, nor does it come with an automatic checkmark once you’re done. This article series will go into detail about how to do proper research for deep learning. We will discuss what types of proposals are needed, what kind of drafts should be written, and how to structure them.
This article will also talk about how to get your draft accepted by most researchers.
Fund your project
Now that you have done some preliminary research into what types of deep learning strategies work, it is time to choose how to approach this topic. You can either do exploratory or formal research.
Exploratory research involves studying theories and concepts in depth to see if they make sense for your area. This could mean reading blogs, doing YouTube videos, looking at past papers, and talking to people who are more experienced than yourself. There are many ways to explore different approaches to understanding a concept!
Formal research means testing those ideas out in reality by conducting experiments or studies. For example, you might test one method against another, evaluate whether or not someone’s theory makes sense, or look up any terms you don’t understand.
By doing both kinds of research, you will know when something works and doesn’t, as well as which methods seem most reliable and trustworthy.
Gain recognition
Doing research in deep learning is much like doing any other type of research. You need to be familiar with what has been done before. If you are looking to advance your knowledge, there are many ways to do so.
There are several strategies that can help you gain recognition for your work. These include publishing papers, presenting at conferences, and giving lectures.
By actively participating in the community, your chances of being recognized will increase! By attending meetings and events, getting involved in online communities, and delivering talks or presentations, you will strengthen your academic profile.
Gaining academic credit through publication requires careful planning and time investment, but it is a valuable way to boost your reputation. There are many free and paid services that can help you manage this process.
Many universities offer formal mentorship programs where experienced researchers will support early career academics. These mentors can be informal (such as from reading journal articles) or more structured (through seminars and/or courses).
Be a scientist
Doing research in deep learning means starting from the fundamentals and building up from there. You can’t just pick up a book that says “deep neural networks!” and expect to understand it, so you have start by understanding other parts of machine learning.
The basics of linear algebra, probability theory, discrete math (graphs, counting), and calculus are all very important pieces of knowledge for doing meaningful work with deep learning.
Once you have those under your belt, diving into more advanced topics is much easier because you will already have some context around what you are studying.
A good way to get started is to read one or two blogs per week that focus on such fundamental concepts as mentioned before.
