Machine Learning vs Deep Learning – Key Differences

Terms like artificial intelligence (AI), machine learning (ML), deep learning today’s hype. However, people often use these terms interchangeably. Although these terms are closely related to each other, they also have distinct features and specific use cases.

Artificial intelligence deals with automated machines that solve problems and make decisions that mimic human cognitive capabilities. Machine learning and deep learning are the two sub-fields of artificial intelligence. Machine learning is artificial intelligence that can make predictions with minimal human intervention. While deep learning is a subset of machine learning that uses neural networks to make decisions by simulating the neural and cognitive processes of the human brain.

The image above shows the hierarchy. We will continue to explain the differences between machine learning and deep learning. It will also help you choose the appropriate methodology based on its application and area of ​​focus. Let’s discuss this in detail.

Machine learning in a nutshell

Machine learning allows experts to “train” a machine by having it analyze huge data sets. The more data a machine analyzes, the more accurate results it can produce by making decisions and predictions of unseen events or scenarios.

Machine learning models need structured data to make accurate predictions and decisions. If the data is not classified and organized, machine learning models fail to accurately understand it, and it becomes the domain of deep learning.

The availability of huge data volumes in organizations has made machine learning an integral part of the decision-making process. Recommendation engines are the best example of machine learning models. OTT services like Netflix learn your content preferences and suggest similar content based on your search habits and viewing history.

To understand How are machine learning models trained?Let’s first look at the types of ML.

There are four types of methodologies in machine learning.

  • Supervised Learning – needs labeled data to give accurate results. It often requires learning more data and periodic adjustments to improve results.
  • Semi-moderated – It is an intermediate level between moderated and Unsupervised education which displays the functionality of both domains. It can give results on partially classified data and does not require constant adjustments to give accurate results.
  • Unsupervised Learning – Detects patterns and insights in datasets without human intervention and yields accurate results. Clustering is the most common application of unsupervised learning.
  • Learning reinforcement The reinforcement learning model requires constant feedback or reinforcement as new information comes in to produce accurate results. It also uses a “reward function” that enables self-learning by rewarding desired results and punishing wrong results.

Deep learning in a nutshell

Machine learning models need human intervention to improve accuracy. On the contrary, deep learning models improve themselves after each outcome without human supervision. But it often requires more detailed and lengthy amounts of data.

Deep learning methodology designs an advanced learning model based on neural networks inspired by the human mind. These models contain multiple layers of algorithms called neurons. They continue to improve without human intervention, much like the cognitive mind continues to improve and develop with practice, revision, and time.

Deep learning models are mainly used for classification and feature extraction. For example, deep models feed into a data set in facial recognition. The model creates multidimensional arrays to save each facial feature as pixels. When you ask him to recognize a photo of someone he hasn’t been exposed to, he readily identifies them by matching limited facial features.

  • Convolutional Neural Networks (CNN) – Convolution is the process of assigning weights to different objects of an image. Based on these assigned weights, the CNN model learns them. The results depend on how close these weights are to the body weight fed as a train set.
  • recurrent neural network (RNN) – Unlike CNN, the RNN model revisits past results and data points to make more accurate decisions and predictions. It is an exact copy of human cognitive functions.
  • Generative Adversarial Networks (GANs) – The two classifiers in a GAN, the generator and the characteristic, access the same data. The generator generates fake data by incorporating feedback from the discriminator. The discriminator attempts to classify whether certain data is real or fake.

Notable differences

Here are some notable differences.

differences machine learning Deep learning
human supervision Machine learning requires more oversight. Deep learning models require almost no human supervision after development.
hardware resources Create and run machine learning programs with a powerful CPU. Deep learning models require more powerful hardware, such as dedicated GPUs.
time and effort The time required to prepare a machine learning model is less than that of deep learning, but its functionality is limited. It takes more time to develop and train data with deep learning. Once established, it continues to improve its accuracy over time.
data (structured/unstructured) Machine learning models need structured data to produce results (except for unsupervised learning) and require continuous human intervention for improvement. Deep learning models can process unstructured and complex data sets without compromising accuracy.
Use cases E-commerce websites and streaming services that use recommendation engines. Advanced applications such as autopilot in aircraft, autonomous vehicles, rover on Mars, face recognition, etc.

Machine learning vs deep learning – which is better?

Choosing between machine learning and deep learning really depends on their use cases. Both are used to create machines with near-human intelligence. The accuracy of both models depends on whether you use relevant KPIs and data attributes.

Machine learning and deep learning will become routine business components across industries. Undoubtedly, artificial intelligence will automate the activities of industries such as aviation, warfare, and automobiles in the near future.

If you want to learn more about AI and how it is constantly revolutionizing business outcomes, read more articles on AI

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