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How does Deep Learning methods compare with traditional Machine Learning methods?

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Related Questions:
– What is Machine Learning?
– What is Deep Learning? Discuss the key characteristics, working and applications of DL

Machine learning vs Deep Learning

Within the field of ‘Artificial Intelligence,’ there exists ‘Machine Learning,’ where we use computer algorithms to make predictions about previously unseen data using patterns and knowledge learned from existing data. Deep Learning is a subset of Machine Learning, and the technique that separates it from traditional Machine Learning is the focus on Neural Networks, systems inspired by the structure and function of the human brain. The term ‘deep’ alludes to the presence of multiple layers within these Neural Networks. Deep Learning is considered a more modern technology than many others within Machine Learning.

Artificial Intelligence, Machine Learning and Deep Learning

Title: Artificial Intelligence, Machine Learning and Deep Learning
Source: Research

Part of the reason why Deep Learning is a newcomer is due to its reliance on high-end computer hardware, which has only become more feasible within the last twenty years. Unlike traditional Machine Learning, which often runs seamlessly on standard business computers, Deep Learning algorithms demand large amounts of data and substantially longer training times.

Deep Learning Algorithms

Traditional Machine Learning Algorithms

10-point difference between Deep Learning and Traditional Machine Learning

SNo.AttributeDeep LearningTraditional Machine LearningWinner
Feature EngineeringLow

Automatically extracts features

Requires manual feature engineering
2Domain ExpertiseLow

Network automatically learns features

Domain knowledge required to design relevant features
3Model ComplexityHigh

Employs deep neural networks with many layers, allowing it to capture complex patterns and relationships in data

Uses simpler models like decision trees, linear regression, or support vector machines (SVMs) to learn patterns in data
4Data requirementsHigh

Requires large amount of data to learn effectively

Can work well with smaller datasets
Traditional ML
5Training timeHigh

Training deep neural networks can be time-consuming, especially for complex architectures

Tends to have faster training times
Traditional ML
6Hardware RequirementsHi-tech

Often requires specialized hardware like GPUs or TPUs to accelerate training

Can be implemented on standard CPUs
Traditional ML
7Interpretability of model predictionLow

More like a black-box model making it challenging to interpret

Very high interpretability for models like logistic regression, decision trees, lower for models such as GBM
Traditional ML
8Performance on simpler tasks with low data requirements

(eg: fraud detection, default prediction, clustering)

Process large amounts of data and learn from it, delivering excellent results

Well-suited for a wide range of tasks. Can be trained on smaller datasets and still achieve good performance
9Performance on complex tasks

(eg: NLP, Image recognition, Speech recognition)

Excels in learning complex patterns and achieving state-of-the-art results

Model limitations to learn highly complex tasks
10Training processMore tedious

Due to high data requirements and longer training times, the training process and hyperparameter tuning in Deep Learning is more challenging
Less tedious

Traditional machine learning involves smaller datasets and shorter training times, allowing for more flexibility in experimenting with a wider range of hyperparameters to enhance model performance.
Traditional ML
Deep Learning vs Traditional Machine Learning
Source: Research

The choice between Deep Learning and Traditional Machine Learning depends on factors such as the nature of the problem, available data, computational resources, and the level of interpretability required. Both approaches have their strengths and weaknesses, making them suitable for different types of tasks. Deep learning is more suitable for complex problems with large amounts of data, while traditional machine learning is more suitable for simpler problems with limited data.

Video explanation (playlist)

  • The first video on ‘Machine Learning vs Deep Learning’ by Levity discusses the key characteristics of the two concepts, their differences and how they fit into the larger AI landscape (Runtime: 8 mins)
  • The second video by Krish Naik, explains the meaning of AI (Artificial Intelligence) and its sub-fields including ML (Machine Learning), DL (Deep Learning), and Data Science. The video substantiates each of these concepts with relevant examples (Runtime: 10 mins)

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