Machine Learning vs Deep Learning

The world of AI is a fascinating place and features lots of new technologies in terms that we are trying to get to grips with.

With artificial intelligence (AI) exploding into our lives this year more than ever before you might be interested to know a little more about the technologies that have been used to create many of the AI tools and services that are currently being developed and released in early development.

Machine learning and deep learning are both subsets of artificial intelligence (AI) that enable computers to learn from data. However, there are some key differences between the two approaches.

Machine learning is a broader term that refers to any algorithm that can learn from data without being explicitly programmed. This includes a wide variety of algorithms, such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on labeled data, while unsupervised learning algorithms are trained on unlabeled data. Reinforcement learning algorithms learn through trial and error.

Deep learning is a specific type of machine learning that uses artificial neural networks (ANNs) to learn from data. ANNs are inspired by the structure of the human brain and are composed of layers of interconnected neurons. Each neuron in an ANN receives inputs from other neurons and produces an output. The connections between neurons are weighted, and these weights are adjusted during the training process.

As you can see, machine learning and deep learning are both powerful tools that can be used to solve a wide variety of problems. The best choice for a particular problem will depend on the specific characteristics of the data and the problem being solved.

Indeed, the differences between machine learning and deep learning can be quite intricate, with each approach offering unique strengths and limitations.

The difference between Machine Learning vs Deep Learning can be intriguing. Deep learning algorithms are generally more complex, requiring a deeper architecture compared to their machine learning counterparts. While machine learning can work with smaller datasets, deep learning requires a large volume of data to perform optimally.

Dr. Deepak Kumar Sahu, President & CEO, VARINDIA