Data science in business is appropriate. It does require an understanding of what you are trying to achieve, however. This interesting information came to us from Built In in their article, “Deep Learning Versus Machine Learning: What’s The Difference?“
To reach the results most are seeking in data science, there are options to consider. Do you utilize deep learning or machine learning to solve different parts of a business problem?
First of all, what is the difference? The easiest takeaway for understanding the difference between machine learning and deep learning is to know that deep learning is machine learning.
Deep learning uses a programmable neural network that enables machines to make accurate decisions without help from humans.
Machine learning utilizes algorithms that parse data, learn from that data and then apply what they’ve learned to make informed decisions.
Which one you choose to use depends on your goals. Before choosing or eliminating deep learning based on the amount of data you have, make sure you’re solving the right problem. Approaching these business goals together as a data scientist can change the way we architect solutions that include either deep learning or other machine learning options (or both).
Melody K. Smith
Sponsored by Access Innovations, the world leader in taxonomies, metadata, and semantic enrichment to make your content findable.
