Machine Learning VS DeepLearning: Settling the age-old debate

Even though the topic is irrelevant to trained machine learning experts, This age-old debate has always been in the spot-light for beginners who are stepping into the AI world.

Even though the conclusion is inconclusive(*wink*) let us try to settle the debate on which one to start with and which one is more useful for beginners and intermediate.

Introduction:

Both machine learning and deep learning are forms of artificial intelligence, however, with some notable differences. While machine learning is a specific application of AI, deep learning is a distinctive form of ML.

In order to make the most out of them, it is important to know how the two subsets of AI differ. Before discussing the various prominent differences between machine learning and deep learning, let’s first get a brief idea about AI, followed by brief descriptions of the two contenders.

Machine Learning — A Specialized Form of AI

Machine learning or ML is a subset of AI. The most fascinating aspect of machine learning is its ability to modify itself when new data is available. This means that ML is dynamic in nature and doesn’t necessitate human intervention for making changes or modifications.

Machine learning makes use of data to feed an algorithm capable to understand the relationship between certain input and output. As soon as the machine completes learning, it can predict either the value or the class of the new data point.

Deep Learning — A Specialized Form of ML

A deep learning model is software that is capable of mimicking the network of neurons found in the human brain. Typically deep learning refers to mostly deep artificial neural networks and rarely to deep reinforcement learning.

The term ‘deep’ in deep learning signifies the number of layers in a neural network. The more layers a neural network has, the deeper it is said to be.

A deep learning machine makes use of various layers to learn from the data provided. While a shallow network has only one hidden layer, a deep network has multiple layers. A typical deep neural network has three types of layers:

• The input layer
• The hidden layer
• The output layer

Machine Learning vs Deep Learning: The Face-Off!

Execution Time

An important distinction between machine learning and deep learning can be drawn in terms of execution time. A typical machine learning algorithm can take anything between less than a minute to a few hours for finishing execution.

Unlike machine learning algorithms, deep learning algorithms require up to several weeks to finish execution.

Feature Engineering

In feature engineering, domain knowledge is used for creating feature extractors. These reduce the complexity of the data as well as enhance the visibility of patterns. The trade-off for the benefits of feature engineering is that it is time-consuming and requires a high level of expertise.

In the case of deep learning algorithms, there is no requirement for understanding the features or best feature that represents the data.

Hardware Dependencies

While ML algorithms work well on low-end machines, deep learning algorithms necessitate powerful machines with multiple GPUs. DL algorithms need to compute a significant amount of matrix multiplication which results in them demanding high-spec systems.

The Approach

Machine learning algorithms are used for parsing data, learning from that data, and make informed decisions based on this very learning. On the contrary, deep learning is used for creating an artificial neural network, capable of learning and making intelligent decisions by itself.

Some Notable Applications of ML and DL

• Computer Vision — Computer vision makes use of ML as well as DL algorithms. Implementations of computer vision include facial recognition and number plate identification.
• Marketing — Automated email marketing, as well as target identification, can benefit from machine learning and deep learning models.

Conclusion:

From the above article, it is clearly known that DeepLearning is an integral and more specialized version of machine learning. However, the computation is more on the demanding side for deep learning.

It is clearly recommended to start off with machine learning and understanding it deeply will ensure the user understands deep-learning algorithms easily and the implementation for solving the problems will be easier.