Recent developments in artificial intelligence have brought us deep learning, an algorithm that has allowed computers to perform tasks usually considered beyond their capabilities. Technically speaking, this is not true anymore! Artificial neural networks are being integrated into almost every field of computer science and technology.
Deep learning algorithms work by feeding large amounts of data through multiple layers of computational units (layers) connected together. The output of each layer is passed onto the next as input, creating deeper levels of computation. At the very end, these layers are pruned away until only meaningful results remain- such as identifying cats or cars.
There are some issues with how quickly deep learning can be deployed, however. When researchers create new architectures of the network, they must test them on several different datasets, making it difficult to publish raw research. Plus, most people are not experienced at developing AI systems, so there is little guidance for those who would like to get involved.
As more and more companies adopt AI, engineers are left with the task of figuring out what tools to use, where to find resources, and how to implement these techniques. This article will go over your basic reading materials for understanding deep learning theories and models. You do not need to be completely familiar with all of them, but knowing enough about one topic can help you understand others!
Reading material: Basic concepts of deep learning
This section contains information about the basics of deep learning.
Read the topic
One of the most difficult things about reading scientific papers is figuring out what the paper actually says. It can be tricky to determine the main points, important concepts, and significant information due to the very nature of science.
When it comes to learning how to read deep learning research papers, however, this is not necessarily the case. Technically speaking, you do not have to understand the whole paper in order to understand the ideas that it contains.
In fact, some of the greatest insights into an idea come from only partially understanding the source material. This applies particularly well to the concept of neural networks discussed in this article!
To make sure you do not miss anything important when skimming through a long paper, therefore, we will go over some basic tips on how to read deep learning papers more efficiently. Keep in mind that these suggestions are applicable whether you are just starting to read academic literature or you are a seasoned reader.
Avoid Getting Too Technical
One common mistake people make when trying to read scientific papers is getting too technical. While advanced mathematics and physics may play a crucial role in certain areas of academia, this is definitely not true for everything.
This goes both ways though; sometimes academics use complicated language and terminology that may mean little or nothing to non-experts.
As mentioned before, one of the key components of many neural network theories is using so-called “neural layers”.
Make a list of important terms
Important term or concept definitions are key to understanding what things mean in a paper. These can include such concepts as neurons, layers, backpropagation, gradient descent, loss functions, etc.
Many papers use these technical terms, but not consistently or clearly. They may also use similar terminology but with different meanings.
By having a clear definition for each term, you will know if your interpretation is correct or not!
Reading a paper more than once can help because now you have some context around those terms.
Make a list of important methods
There are several strategies you can use to read deep learning papers effectively. Many of these have been mentioned in other articles, so we will go more in-depth here!
First, make sure to understand what each section or paragraph is talking about. The abstract, introduction, and conclusion should all include enough information to give a basic understanding of the paper’s topic and importance.
After that, learn how different styles of writing contribute to the paper’s effectiveness. A well written paper uses appropriate vocabulary, structures, and emphasis to emphasize key points.
Read the methods closely
Even if you are not familiar with every aspect of neural networks, reading the paper thoroughly is still important!
A lot of people get stuck when trying to read a deep learning paper because they don’t know what to look for or how to evaluate what they find.
There are certain components that everyone includes in their model, but there’s no clear explanation as to why they include them and whether it makes sense to add them.
Intermediate concepts like activation functions or regularization terms may be missing or explained poorly.
What matters most isn’t just knowing what a term means in general, but understanding how it applies to your specific model.
In this article we will go over some basic tips to help make sure you understand the models you’re looking at, and how to apply those insights in the real world.
Look for a research question
As we have discussed before, most papers contain a research topic or goal that they address. What makes a good paper is not only their argument but also how they use it to ask a question. A well-asked question is important because it gives the reader a sense of what the paper is trying to get at.
A good starting point to find such a question is by looking at the conclusion of the paper. Many conclusions summarize the findings of the paper and state the main takeaway. For example, in one of our previous blogs posts, you learned how to determine if an article contains solid information using an evidence checklist. The conclusion of this paper lists the two types of evidence used to confirm the claim.
Types of Evidence: statistical and rhetorical
So, while reading a new paper, look to see whether there are any results or examples that could potentially prove or disprove the claims made. These can be quantitative (like numbers) or qualitative (meaning no number).
In addition to looking for a summary of the paper’s findings, try to understand why the authors wrote the way they did. Were they supporting something? Or, did they take a contrary position and argue against it? By understanding the motivation behind writing materials, you will learn more about the author and the potential strengths or weaknesses of the paper.
The last step here is to compare your answers with those of other people.
Read the results
A lot of people get stuck trying to read deep learning papers because they cannot understand what the authors are talking about. They assume that the author is just using very complex language or mathematical formulas, so them looking at the result sections and figuring out what things mean seems like an easy solution.
But reading those pages and pages of text can be tricky!
The first time I tried to read through a paper’s results section was for a graduate level research seminar. It took me several minutes to figure out what each part of the equation meant, and even then I had to look up some terms in order to make sense of it.
That isn’t the case for most researchers – if anyone knows how to do something beyond trivial examples, then their readers will too.
So, don’t try to guess what every line means until you have worked your way through an example few times. There may be further explanations hidden somewhere else in the article, or someone more experienced could drop by and comment as well.
Look for the discussion
A paper that is well written will have an underlying theme or topic. The topic should be clear as the paper itself, and you should be able to tell whether it was discussed in the paper.
If there are lots of examples with no explanation, then the paper may be more about how the writer wrote their example rather than what they were trying to get across.
There could also be too much talking around the topic, which would make it hard to understand who and what the author wanted to address.
Reading a paper carefully and thinking about the topics and points made can help you here.
Read the conclusion
The main takeaway from this article is that reading deep learning papers isn’t very difficult if you follow some basic rules. By now, you should be familiar with these tips!
So what are those tips? They’re simple — read the conclusions of each paper.
Why is that such a good idea? Because the conclusions tell you something about the content of the paper and why it was written. They also usually summarize the key takeaways or ideas in the paper.
And since most people don’t do that, they miss out on important information. In fact, there’s an excellent chance that someone else has already summarized the paper for you here on our website!
Take a look at two examples:
In this paper, the authors set out to determine whether listeners could understand spoken paragraphs more easily when the words were underlined or not. (Underlining can help emphasize certain parts of a sentence.)
They found that, contrary to popular belief, underlining does not make speech easier to listen to. In fact, it can even hurt your listening experience because it may add “noise” to the signal coming through your earbuds.
However, the researchers did find one small change that made speaking much clearer: prefixing every third word with either a capital or lowercase letter.
