Understanding the latest advancements in artificial intelligence (AI) can be overwhelming since its appearance during the first half of the 20th century. AI innovations come down to two main concepts: Machine learning and Deep learning. The emerging ability of machines to learn as they go unlocks the potential and evolvement of technology these days.
What are these concepts that have dominated the technological era of artificial intelligence? How are they different from each other?
The easiest takeaway for understanding the difference between machine learning and deep learning is to know that deep learning is a subset of machine learning.
A research report by Gartner predicted that by 2020, almost around 85% of customer engagement will be non-human and the market for artificial intelligence has grown to approx. $5.05 billion.
Are you curious enough to know how closely-knit concepts are different from each other? Let’s try to understand.
All instances where a machine learns are counted as machine learning but differ to that with deep learning. Machine learning uses algorithms that parse data, learn from that data, and apply what they have learned to make informed decisions. When we talk about deep learning, it implies a specific method of enabling a machine to learn and make decisions. It resembles the neural networks that exist in the brain.
For instance, you have some pictures of cats and dogs and you need to identify them separately using machine learning and deep learning algorithms.
With machine learning, you simply need to mark the images of dogs and cats to determine the characteristics of both the animals. The availability of structured data will be sufficient for a machine-learning algorithm to work and categorize between the two.
With deep learning, it works similar to the human brain to solve problems. It doesn’t require structured data to classify images. The neural networks here hierarchically determine the specific features of the images and classify them accordingly.
- Machine learning algorithms are designed to “learn” and act by understanding the labeled data and use it to produce new results with more datasets.
- While deep learning networks do not require any human intervention, however, it can be incorrect when data provided is faulty at times.
- With a machine learning algorithm, if an inaccurate prediction is made human intervention is required whereas it’s not the case with deep learning. It can determine on its own through its neural networks.
- Due to the requirement of much more data sets in deep learning algorithms, they require powerful hardware systems than the machine learning systems.
- While machine learning algorithms analyses data into multiple parts and then combines to a solution, deep learning algorithms look into the entire problem at once.
Worth Noting: The difference between deep learning and machine learning is just as the difference between your thumb and the fingers where deep learning is the thumb and machine learning is the fingers. All deep learning is machine learning but not all machine learning can be deep learning.
But what differentiates the two exactly is how they work.
The development of artificial intelligence has also generated growth in software development services as well as IoT applications. And businesses are also on a lookout for more software consulting companies for much growth and expansion.