Machine learning and deep learning are both core technologies of artificial intelligence. There are key differences between them, however; machine learning is more advance and is used to solve complex problems quickly, while deep learning requires more time and resources to set up and analyze, but provides more comprehensive and accurate results. While deep learning can learn from mistakes and adapt to do better next time, machine learning can detect patterns quicker as it applies to a smaller data set.
Machine learning algorithms can be used to data to spot patterns with facial recognition, cybersecurity, and data analytics while deep learning provides the ability to tackle problems of complexity similar to those that humans can solve. Designing deep learning systems requires multiple processing layers to extract high-level insights from data and understands problems better by using machine learning algorithms.
There are endless applications for machine learning. Common uses include analytics, rapid processing, calculations, facial recognition, cybersecurity, and human resources. Deep learning can be used to generate text, deliver meeting transcripts, capture data from documents, and generate video content from text.
Ultimately, machine learning and deep learning can be regarded as successful tools for assisting humans in addressing problems and eliminating tedious manual labor. Both technologies are critical components of the development of a more technologically advanced and autonomous future.
