Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
If you are just starting out in the field of deep learning or you had some experience with neural networks some time ago, you may be confused. I know I was confused initially and so were many of my colleagues and friends who learned and used neural networks in the 1990s and early 2000s.
The leaders and experts in the field have ideas of what deep learning is and these specific and nuanced perspectives shed a lot of light on what deep learning is all about.
- Has best-in-class performance on problems that significantly outperforms other solutions in multiple domains. This includes speech, language, vision, playing games like Go etc. This isn’t by a little bit, but by a significant amount.
- Reduces the need for feature engineering, one of the most time-consuming parts of machine learning practice.
- Is an architecture that can be adapted to new problems relatively easily e.g. Vision, time series, language etc., are using techniques like convolutional neural networks, recurrent neural networks, long short-term memory etc.
- Requires a large amount of data — if you only have thousands of example, deep learning is unlikely to outperform other approaches.
- Is extremely computationally expensive to train. The most complex models take weeks to train using hundreds of machines equipped with expensive GPUs.
- Do not have much in the way of strong theoretical foundation. This leads to the next disadvantage.
- Determining the topology/flavor/training method/hyperparameters for deep learning is a black art with no theory to guide you.
- What is learned is not easy to comprehend. Other classifiers (e.g. decision trees, logistic regression etc) make it much easier to understand what’s going on.
Cost and time benefits
Neural networks are trainable “brains.” You give them your company’s information and train them to do a task, such as generating reports, and they will use that training, new information, and their “work experience” to adapt and improve in much the same way a human worker learns.
Unlike a human worker, however, these software robots work at a much faster rate and never sleep. The utilization of deep learning in your business can save your company money spent in hiring extra employees or outsourcing for specified projects. It can also save your employees time.
When repetitive or time-consuming work is done quickly and efficiently at the push of a button, your employees are freed up to do the creative work that will help your company grow. Let’s talk more about that next.
Quality and accurate results
Using deep learning, your software robots can recognize more data and images, understand spoken language, overcome problems, and work more efficiently.
Image recognition algorithms are becoming increasingly accurate, and AI is becoming more widely used. Through the use of deep learning, your company will avoid common errors and save the time normally spent fixing them.
With the improvements in intelligent automation, your employees might become concerned about their jobs, but they actually have the potential for growth.