While both machine learning and deep learning sound similar, there are some key differences between the two

In the coming years, surviving in either industry or academics with deep learning and machine learning abilities will play an important role. It is difficult to grasp the latest artificial intelligence (AI) developments. Still, if you're keen to learn the fundamentals, you can break many AI technologies down into two concepts: machine learning and deep learning.

Differences Between Machine Learning and Deep Learning

While both machine learning and deep learning involve algorithms that learn from data, there are some key differences between the two:

Definition

Machine learning is an artificial intelligence (AI) technology that automatically allows systems to learn and develop from experience without being explicitly programmed.

Deep learning is a concept of artificial intelligence (AI) that mimics the functioning of the human brain in data processing and the development of patterns for decision-making use.

Focus

Machine Learning focuses on the growth of software programs that can access and use knowledge to understand for themselves. Deep learning is an artificial intelligence subset of machine learning with networks that learn without being managed from unstructured or unlabeled data. It can also be called Deep neural learning or a deep neural network. 

Complexity

Deep learning is more complex than machine learning, as it involves neural networks with multiple layers. This complexity allows deep learning models to learn more complex patterns and relationships in data.

Data Requirements

Deep learning requires more data than machine learning, which involves more complex models. This can be a challenge for organizations with limited data resources.

Hardware Requirements

Deep learning requires more powerful hardware than machine learning, as it involves neural networks with many layers. This can be a barrier to entry for organizations without the necessary hardware resources.