Deep learning vs Machine learning

DL and ML

Understanding the latest trends in artificial intelligence (AI) can seem challenging, but it really boils down to two terms you’ve likely heard of before: machine learning(ML) and deep learning(DL). These terms are often used in ways that can make them seem interchangeable, that is why it’s important to understand the differences. To know the way these terms correlate and, perhaps, differ, let’s start with looking deeper into both of this concepts.

Wikipedia says, that ML is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

To put things simple, ML is the process when computers learn from their experience, just like people’s brains do. There are mainly two types of ML techniques: supervised learning, which finds patterns and develops predictive models using both, input data and output data. And unsupervised learning, which finds patterns only on input data. This technique is useful when you’re not quite sure what to look for. Often used for exploratory Analysis of raw data.

Now let us take a look at DL, deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making.

In simple terms, deep learning is just a subset of machine learning. It technically is machine learning and functions in a similar way, that’s why the terms are sometimes improperly interchanged. But, still, its capabilities are different.

Basic machine learning models do become progressively better at whatever their function is, but they still need some guidance. If an ML algorithm returns an inaccurate prediction, then an engineer needs to step in and make adjustments. But with a deep learning model, the algorithms can determine on their own if a prediction is accurate or not.
How does deep learning work?

A deep learning model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. To achieve this, deep learning uses a layered structure of algorithms called an artificial neural network (ANN). The design of an ANN is inspired by the biological neural network of the human brain. This makes for machine intelligence that’s far more capable than that of standard machine learning models.

It’s a tricky prospect to ensure that a deep learning model doesn’t draw incorrect conclusions, but when it works as it’s intended to, functional deep learning is a scientific marvel and the potential backbone of true artificial intelligence.

A great example of deep learning is Google’s AlphaGo. Google created a computer program that learned to play the abstract board game called Go, a game known for requiring sharp intellect and intuition. By playing against professional Go players, AlphaGo’s deep learning model learned how to play at a level not seen before in AI, and all without being told when it should made a specific move (as it would with a standard machine learning model). It caused quite a stir when AlphaGo defeated multiple world-renowned “masters” of the game; not only could a machine grasp the complex and abstract aspects of the game, it was becoming one of the greatest players of it as well.

To sum it all up, here is the list of main differences between ML and DL:

• Machine learning uses algorithms to analyze data, learn from that data, and make informed decisions based on what it has learned;
• Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own;
• Deep learning is a subfield of machine learning. While both fall under the broad category of artificial intelligence, deep learning is what powers the most human-like artificial intelligence

Thus, summarizing the above, we can say with reasonable confidence that Deep learning is ML, it is the next evolution of ML– it is how machines can make their own accurate decisions without human interference.