One of the biggest questions among data scientists is the following: ‘has deep learning won over the traditional machine learning?’ In many cases, this type of learning is the best way to assure the win, but there are still several misunderstandings we have to clear up before we answer this question.
Distinguishing Deep Learning and Machine Learning
What many people fail to understand is that these two terms are actually not as different as we think. The distinction between deep learning and machine learning lies in the fact that one is a subset of the other. You probably guessed which one is which.
Deep learning is a subset of machine learning.
Still, in order to help you differentiate between the two, we decided to introduce you to both learning types separately.
Machine Learning
Machine learning applies to any form of a computer-enabled algorithm used to find a pattern in a data, by being applied against a data set. This encompasses a lot, basically all data science algorithms types you can think of. Therefore, starting from supervised to segmented and classified data, this all falls under machine learning.
Deep Learning
Deep learning is a variation of the Artificial Neural Nets. This is a subset of machine learning with at least 28 unique ANNs architectures. The architectures with multiple hidden layers within are what make this learning ‘deep’. One of the best learning opportunities for deep learning is participating. This is all you need to be acknowledged. For this reason, many websites use deep learning to rank high on the leader boards. They have unstructured data lent into Deep Learning algorithms such as RNNs and CNNs. At the end of the day, it is the RNNs and CNNs carrying the day.
Benefits and Drawbacks of Deep Learning
Many of the unique architectures for ANNs are specialized as those layers used in RNNs and CNNs. Therefore, if you have a business with an image of NLP unstructured data, using these two is the best way to go. Despite this, there are several disadvantages that come with using deep learning. These drawbacks include:
- It will take you a long time to develop RNN and CNN from scratch;
- RNNs and CNNs can often fail to train because this is extremely difficult to accomplish;
- RNNs and CNNs require plenty of labeled data, which can be costly if you decide to acquire.
Due to the costly and complex creation of new RNNs and CNNs, the market starts turning towards prebuilt models available via API, found on Amazon, Google, IBM and Microsoft.
If you read a solid writing blog regarding this matter, you will find that there are numerous options for getting a new RNN and CNN without the extra hassle. Generally speaking, this approach can do wonders if you are trying to implement Deep Learning in an automated customer service or other in-house systems. However, be aware that these algorithms mean not deploying software solutions. Instead, they use a hard-coded algorithm in a special purpose chip. Such chips include FPGAs and GPUs.
Predictions of Customer Behavior on the Data Science Markets
Nowadays, more than 80% of the data science application goes in the prediction of consumer behavior. Research serves to check when the consumers come, why they keep interested, what is their next step, etc. By providing the real data science market with such statistics, they are now able to make the best solutions to existing problems, recommend solid purchases and offer the best CSR conversations.
If you check the commonplace supply chain forecasters that work on time series data, geospatial algorithms for market and site planning, as well as equipment monitoring solutions, you will find that the traditional machine learning is far from dying out on the market.
Even though Deep Learning is forced among many companies and can be directed towards a certain class of problems, they are not always the most suitable option. In many cases such as these ones, all applications can be easily addressed by the traditional machine learning approach.
All things considered, you can build a perfect model in a timeframe of only few minutes or hours. Deep Learning allows this in places where it is suitable seen from an efficiency standpoint, which is a huge advantage and a great investment in time. By using these strategies, you do not need to wait for months for a solution.
Still, the answer remains no. Deep Learning will not eliminate Machine Learning or make it obsolete, at least not in the near future. We need both techniques to achieve optimal results and every data scientist should know this. If you want to master data and achieve high results, you need to learn both the Deep Learning and traditional Machine Learning techniques.
