Deep Learning Deep Dive
In the age of AI implementation, the current period of AI we find ourselves in, we must understand the pros and cons of both machine learning (ML) and deep learning (DL) in order to decide when to use either technology. Some other terms you might have come across with respect to AI/ML tools are applied AI and deep tech. Again, both ML and DL are subsets of AI but operate at different levels of complexity and applicability. Traditional ML models are best suited for structured data (data that is labeled) and are able to make predictions or decisions without being explicitly programmed to do so. DL, on the other hand, represents an advanced evolution of ML that uses artificial neural networks (ANNs) to analyze and interpret much more complex, unstructured data.
We briefly touched on these concepts in Chapter 1 but this distinction is important to mention here as well. With structured datasets, data scientists and ML engineers use the structure of the data...
