Why are ML and DL so powerful?
Although most AI fields are flourishing and gaining more attention recently, ML and DL have been the most influential fields of AI. This is because of several factors that make them distinctly a better solution in terms of accuracy, performance, and applicability. In this section, we are going to look at some of these essential factors.
Feature engineering
In traditional AI, it is compulsory to design the features manually for the task. This process is extremely difficult, time-consuming, and task/problem-dependent. If you want to write a program, say to recognize car wheels, you probably need to use some filters to extract edges and corners. Then, you need to utilize these extracted features to identify the target object. As you may anticipate, it is not always easy to know what features to select or ignore. Imagine developing an AI-based solution to predict if a patient has COVID-19 based on a set of symptoms at the early beginning of the pandemic. At that time, human experts did not know how to answer such questions. ML and DL can solve such problems.
DL models learn to automatically extract useful features by learning hidden patterns, structures, and associations in the training data. A loss is used to guide the learning process and help the model achieve the objectives of the training process. However, for the model to converge, it needs to be exposed to sufficiently diverse training data.
Transfer across tasks
One strong advantage of DL is that it’s more task-independent compared to traditional ML approaches. Transfer learning is an amazing and powerful feature of DL. Instead of training the model from scratch, you can start the training process using a different model trained on a similar task. This is very common in fields such as computer vision and natural language processing. Usually, you have a small dataset of your own target task, and your model would not converge using only this small dataset. Thus, training the model on a dataset close to the domain (or the task) but that’s sufficiently more diverse and larger and then fine-tuning on your task-specific dataset gives better results. This idea allows your model to transfer the learning between tasks and domains:
Figure 1.3 – Advantages of ML and DL
Important note
If the problem is simple or a mathematical solution is available, then you probably do not need to use ML! Unfortunately, it is common to see some ML-based solutions proposed for problems where a clear explicit mathematical solution is already available! At the same time, it is not recommended to use ML if a simple rule-based solution works fine for your problem.
