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Deep learning>

<Meaning>

what's deep learning and how does it work?

Deep learning is the subset of machine learning methods based on artificial neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. In image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.

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<01> Applications

Deep learning can be used in a wide variety of applications:

// Image recognition

Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing and actions in digital images. Computers can use machine vision technologies in combination with a camera and artificial intelligence (AI) software to achieve image recognition.

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// Natural language processing

Natural language processing (NLP) uses machine learning to reveal the structure and meaning of text. Organizations can analyze text and extract information about people, places, and events to better understand sentiment and customer conversations.

<02> Future of d.l

The possible future and use of deep learning in future society...

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// Support and commercial

Support and growth of commercial activities over the networks. NLP and digital marketing have increased the use of deep learning algorithms and gained valuable attention from consumers.

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// automate repetitive tasks

An urge to automate repetitive tasks requiring more physical labor than mental involvement will encourage data scientists and engineers to innovate in AI continuously.

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Neural networks>

<Substance >

what ARE NEURAL NETWORKS AND HOW DO THEY WORK?

A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.

Neural networks rely on training data to learn and improve their accuracy over time. Once they are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts.

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<01> Employments

Uses of neural networks in the society.

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// HEALTCHARE

Nowadays, healthcare personnel can easily use voice recognition features to get the patient's information, thanks to neural networks.

// IMAGE CLASSIFICATION

You can train neural to recognize and classify objects into images. Additionally, you can feed them a large number of pictures and optimize them for accurate facial recognition. Thus, making them useful for tasks such as facial recognition.

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<02> upcoming

Development of neural networks in the future.

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// HYBrid architecture

In the future, we can see hybrid architecture that combines different neural networks and deep learning techniques to build an integrated computer program. These hybrid architectures might also address some of the limitations related to neural and improve overall performance.

// Transfer Learning

Transfer learning is the process of re-using pre-trained models on a new problem. It is a popular trend in deep learning because it can train neural networks with little data. The rapid use of neural in transfer learning models makes it clear that the trend is here to stay. Therefore, in the future, you can expect more research-focused transfer learning models.

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Thank you!