Decoding Machine Learning vs Deep Learning: Understanding the Contrasts:-

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Jan 5, 2024

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If you are interested in building your career in the IT industry then you must have come across the term Data Science which is a booming field in terms of technologies and job availability as well. Here, we will learn about the two major fields in Data Science that are ML and Deep Learning. So, that you can choose which fields suit you best and is feasible to build a career in.

Machine Learning:-
Machine Leaning allows the computers to learn from the experiences by its own, use statistical methods to improve the performance and predict the output without being explicitly programmed.
It involves training algorithms on large datasets to identify patterns and relationships and then using these patterns to make predictions or decisions about new data.

DEEP LEARNING:-
Deep learning is a specialized form of machine learning that teaches machines to do what humans are naturally born with: learn by example.
Though the technology is often considered a set of algorithms and networks that aim to ‘mimic the brain’, a more appropriate description would be a set of algorithms and networks that ‘learn in layers’.

Combining Deep Learning with Reinforcement technology powers innovations like driverless cars, voice-controlled devices, robotics, and more.

Similarities:-
· AI Techniques: Both are subsets of data science and AI.
· Statistical basis: DL and ML both use statistical methods to train. e.g. regression analysis, decision trees, linear algebra, and calculus.
· Large datasets: Both ML and DL require large sets of quality training data to make more accurate predictions.

Key differences:-
· Intended use cases: ML identifies patterns from structured data, such as classification and recommendation.
· DL solutions are more suited for unstructured data, where a high level of abstraction is needed to extract features.
· Problem-solving approach:
In ML, humans select and extract features from raw data and assign weights. DL solutions perform feature engineering with minimal human intervention.
· Training methods:
ML: supervised , unsupervised, semi-supervised, and reinforcement learning.
DL: convolutional neural , recurrent neural , generative adversarial networks, and autoencoders.
· Performance:
Both ML and deep learning have specific use cases where they perform better than the other.
· Infrastructure: ML : Run on a single instance or server cluster, DL: Requires high-performance clusters.
· Outputs: ML: Numerical Value, like classification of the score.DL: Anything from numerical values to free-form elements, such as free text and sound.

In conclusion, we can say that deep learning is machine learning with more capabilities and a different working approach. And selecting any of them to solve a particular problem is depend on the amount of data and complexity of the problem.

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