Machine Learning Vs Deep Learning

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In 1950’s Artificial Intelligence emerged making the Information Technology more feasible to humans and human-related tasks happen more easily and accurately. In 1980’s, a new subset emerged out of Artificial Intelligence called as Machine Learning making use of human inputs, it analyses and gives output more accurately. Now comes another subset of AI via Machine language called as “DEEP LEARNING”

The technology is fundamentally altering the way we live, work, and communicate akin to the industrial revolution – more specifically making us think less and providing information within no time.

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The shortest Explanation:

Artificial Intelligence: Computer system(s) that replicates human intelligence.
Machine Learning: Makes computers to perform a self-learning.
Deep Learning: Algorithms attempting to model high-level abstractions into data to determine a high-level meaning.

Let’s Go Deeper into Machine Learning vs Deep learning

Machine Learning	Deep Learning
It is the subset of Artificial intelligence.	It is a subset of Machine learning.
It covers all types of data science algorithms and pattern recognitions.	It covers neutral networks covering network layers and parameters.
These algorithms can work on low performance computers without GPU as there is no need to store any data.	These algorithms required high performance computers with GPUs and TPUs with huge storage power.
They breakdown the problem into pieces and solve them individually and then combine it.	They solve the problem end-to-end.
They required features to be identified and then coded manually	They try to identify high-level features from low-level features.
They have a defined set of rules that can be understandable.	They have mathematically complicated rules which are difficult to understand.
They take more time in testing phase rather than training the model.	They take more time in training the model rather than testing.
The optimal data volumes would in thousands	The volumes of data would be in millions as it deals with big data.
The output can be in numerical like classifications or scores.	The output can be anything- numerical, free-form elements like test, image videos or sound.
It uses various types of automated algorithms and learn to model functions and predict future actions from data.	It uses neural networks that passes data through many processing layers to interpret data about the features and their relationships.
These algorithms are directed by Data analysts	These are self-directed.
It allows a systems to recognize patterns on their own and make future predictions.	It uses some machine learning techniques connecting neural networks that simulate human decision-making.
They can quickly be applied on facial, speech, object recognitions, translation, and many other.	They can be applied on image , video, sound and text recognitions etc.
	It can be expensive and requires huge datasets to train itself.
It requires someone to continuously code or analyze data to solve a problem and predict a result.	It is a bit automatic.

The Future of Deep Learning

Though Deep learning is the long way to go, some big companies are encouraging it to gain competitive edge.A Philadelphia-based TV & movie company is using deep learning to develop the new product that has a has a voice-controlled remote. Comcast is applying deep learning technique to break the video content into "chapters" and generate natural-language summaries for each chapter automatically.
Know more about Deep learning from our upcoming posts.

Frequently Asked Machine Learning Interview Questions

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Last updated: 08 Oct 2024

About Author

Ravindra Savaram

Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.

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