A Basic Explanation of Deep Learning

A Basic Explanation of Deep Learning

They aMachines or computers can learn. Exposing or training them to datasets enable them to make predictions or produce desired outcomes. This is called machine learning. A more advanced application of this subfield of artificial intelligence is called deep learning.

Understanding What Deep Learning is and How it Differs from Traditional Machine Learning

Explaining the General Working Principle

Remember that deep learning is a more advanced subfield and model of machine learning. Non-deep learning models generally need learning algorithms, training datasets with annotations, and test datasets for validating accuracy.

Deep learning models, on the other hand, depend on Big Data or large datasets. They can recognize patterns in the data and produce more accurate predictions or relevant outcomes.

The capabilities of these models stem from their use of artificial neural networks. These models are based on algorithms or architectures with multiple hidden layers. An artificial neural network is an algorithm modeled after a biological neural network.

Note that traditional models have shallow networks with one to two hidden layers. Multiple layers can perform much more complex processing and representation of data.

Based on the aforementioned, traditional or simpler models require manual feature engineering while deep learning models can learn useful representations of data on their own because they can analyze complex and non-linear relationships.

Importance in AI and Notable Applications

Machine learning is one of the main subfields of artificial intelligence. Its purpose is to equip machines or computer systems with the capabilities to learn from data and perform tasks without being explicitly programmed.

The introduction of deep learning has advanced further the applications of machine learning. This has also advanced other subfields of artificial intelligence.

For example, sophisticated models that use artificial neural networks are central to natural language processing using large language models, computer vision through advanced image recognition and image processing, and sophisticated robotics.

These models are also behind notable NLP applications such as generative AI applications including ChatGPT and Dall-E or writing tools such as Grammarly and Quillbot.

They are also responsible for advancing self-driving vehicles, equipping smartphones with computational photography features, more accurate speech and text recognition and conversion, and virtual assistant applications.

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