Introduction
Deep learning is one of the most exciting developments in machine learning and AI. The technology can help businesses make an incredible impact on their bottom line by helping them gain key insights about their consumers. However, for deep learning to be successful, it must be implemented with care and with a thorough understanding of its abilities and limitations.
Find the right application.
The truth is, deep learning isn't a replacement for human intelligence. It's not intended to be.
The same human intuition and creativity that has led you to where you are now won't change with the advent of deep learning—and they shouldn't.
While it's true that deep learning can help your business find valuable solutions in data sets, only humans can truly interpret those results and make them actionable, which leads us right back to where we started: finding the right application.
Find a use case that produces results.
When it comes to deep learning and AI, there's a lot of hype. But before you jump on the bandwagon, take some time to think about whether your use case is a good fit for deep learning. If you're not sure where to start, here are some questions that can help you determine if this technology is right for your business.
- How will my customers use the result?
- Do I have enough data available?
To get started on this process, consider what value your organization would like to derive from using deep learning or AI in its business processes. You want something that has an impact on customers and serves their needs—not just something cool.
Make sure you have the right data.
You should be able to answer the following questions about your data:
- Is it labelled? If not, how do you know what each data point represents?
- Are the labels correct? If not, how can your system learn from them? Can you fix them? How much time do you have to spend fixing them before moving on to other tasks (like building a model)? In addition to being correct, are they useful for solving your problem(s) of interest and making decisions that will improve outcomes.
Ensure that results are being examined.
Testing the model for accuracy, efficiency, cost and user experience is crucial to ensure that your enterprise gets a high-quality product. Therefore, how important is this phase can’t be stressed enough at all!
- Accuracy: Ensuring that results are being examined
- Efficiency: Ensuring that the model works quickly
- Cost: Ensuring that the output is inexpensive or free to use with little overhead costs
- User Experience: Ensuring that the end-user can interact with the model in an intuitive way so they don't get frustrated when using it
Determine if enough data is available to train the model.
The first step in building a deep learning model is to determine if enough data is available for you to train the model. If you don't have enough data, then there are multiple ways that you can use synthetic data:
- Use representative samples from existing datasets (e.g., the ImageNet dataset) or collected directly via sensors on a device
- Generate additional training images and labels by taking a subset of your existing datasets and augmenting them with noise, distortions and occlusions
Choose your approach cautiously.
Deep learning is a powerful tool that can be used to solve many different problems. However, it's not always the right approach, and you should choose the right approach for your problem. If you can solve a problem with deep learning and nothing else, then great! But if you need to combine multiple approaches to get desired results, don't hesitate to do so.
Keep in mind that not every solution needs deep learning.
If you are working with a simple problem, you may be able to solve it with other methods like traditional machine learning and classical programming.
Additionally, problems that do not have a lot of data are not suitable for deep learning. For example, we would not expect to use deep learning to create a model that predicts the gender of an author based on their writing style. In this case, the number of possible inputs (authors) is too small relative to the number of potential outputs (gender).
Deep learning can create incredible results when used in the right way and only after proper consideration of its impact on your business.
While it can be tempting to jump on board with the latest trend, deep learning is not a magic bullet. There’s no question that when used correctly, machine learning can produce incredible results in a variety of industries. But if you want your organization to get the most out of its data, you have to make sure it’s used appropriately and only after proper consideration of its impact on your business.
Conclusion
We hope that by now we’ve convinced you of the possibilities of deep learning and given you some clear steps to follow in order to implement it successfully. This is a complex topic, but with the right expertise and knowledge, you can reap some incredible benefits. And at the end of the day, that’s all we really want—to help your enterprise succeed!
