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9 Reasons Why AI Is a Game-Changer in Deep Learning

Artificial intelligence is a very vast field that is rapidly developing. The objective is to train machines to learn and act like humans to accomplish tasks efficiently - hopefully in every sector. AI is the umbrella under which subsets like deep learning and natural language processing fall. Deep learning uses neural networks that function similarly to the human brain’s neurons in order to perform complex tasks.

The applications of deep learning can be seen every day, whether it’s when using the face unlock feature on mobile phones, performing language translations, using recommendation systems, and much more. Deep learning-based automatic text generation and summarization are also gaining popularity.

This article will discuss how and why AI in deep learning is a definite game-changer.

Works magic on massive volumes of data

The engagement on social media platforms like Facebook, Twitter, and Instagram - in the form of comments, tweets, videos, and images - leads to huge amounts of data generation. Digital marketers target their customers through sponsored social media content and also collect data on the interaction of customers with said content.

Conventional ML models are sufficient when dealing with lesser volumes of data. But as data volumes increase, the accuracy of ML algorithms peaks and then degrades, creating the need for deep learning. In recent years, deep learning models have gained more popularity and demand owing to their superior performance on large volumes of data.

Deep Learning models vs ML techniques_11zon.webp
Image source

Flexibility and self-learning capabilities

The biggest advantage of deep learning models is that they can work on unstructured data, unlike classic ML models. They have the capability to process data in the form of text, audio, images, and more.

The family of convolutional neural networks is specifically designed to efficiently work with images and videos. These networks manage to extract important features with minimal computational costs. DL models can generate new features without human intervention. This kind of automatic feature extraction gives them the upper hand.

End-to-end problem-solving

The approach adopted by deep learning models toward any task is usually end-to-end. An excellent example is object detection with YOLO (You Only Look Once). Justifying its name, the YOLO model can input an image, perform segmentation, and detect objects and locations in a single go. This is possible due to the complex multi-layered architecture of neural networks.

In neural networks, ;earning occurs in incremental steps across hidden layers. The initial layers learn to extract simpler features like edges and lines in case of image input, whereas the deeper layers learn more in-depth features like shapes, colors, and so on. The image below is an example of object detection with YOLO V5.

YOLO Object Detection (1).webp
Image source: https://www.v7labs.com/blog/yolo-object-detection

Parallel computing and cost-effectiveness

Training deep learning models requires graphical processing units (GPUs) and good hardware. And although training time is relatively long, the models still contribute to cost reduction. In large businesses, inaccurate predictions or error margins can have devastating consequences. But with deep learning models, this can be minimized. Training DL algorithms parallelly through distributed systems also helps reduce computation time as data is distributed across multiple machines and trained simultaneously.

Scalability and modularity

Deep learning algorithms are highly scalable and can work effectively in large businesses. DL is also remarkably modular, which makes it easier to adapt codes for different purposes without having to recreate them from scratch. Pre-trained deep learning models based on transformers like BERT and GPT 2 can be finetuned and used on many day-to-day problems. Google Cloud also provides a platform for better model organization and training.

Transforming the healthcare domain

Deep learning is steadily transforming the healthcare sector. Case in point: Qure.ai, a startup that makes AI-based products to assist in the field of radiology. qXR, one of its products, is capable of detecting abnormalities or indications of a wide array of diseases from chest x-rays in less than a minute.

qXR uses different deep learning models to train on x-rays obtained from patients and predict infections. Such products can be linked to simple mobile apps and used in rural areas too where there is a shortage of radiologists and technicians. In this way, deep learning can make quality healthcare accessible nearly everywhere in a matter of minutes.

The image below is an example of how a 3D deep learning model is used for medical image classification of brain MRIs.

3D Deep Learning model used for image classification.webp
Image source: https://arxiv.org/pdf/2004.00218

Assisting marketing analytics

Statistical and classic models have traditionally been used in business to predict customer lifetime value, shopping patterns, etc. In recent years, it’s been observed that deep learning performs better than traditional statistical modeling in these situations.

The nonlinear layer structure of neural networks enables them to better predict changing customer patterns and trends. It provides a more comprehensive view of the customer through interlinked variables. In addition, deep learning can handle verbal inputs which significantly improves the scope of prediction.

Usage in research activities

The applications of deep learning are not limited to object detection and speech recognition; the technology is also a major part of research developments in materials science, physics, biotechnology, etc. As deep learning enables analysis of unstructured data and captures complex relationships, it has applications in spectral data, atomistic modeling, and so on.

In metallurgical and materials science, artificial neural networks are used to model and predict the flow stress of austenitic stainless steel as a function of various experimental parameters like strain rate, temperature, etc.

The rise of self-driving cars

A great example of how deep learning could create a new reality in the near future is with self-driving cars. DL is used in every part of autonomous driving vehicles including localization, perception, prediction, and decision-making. On a simple level, neural networks are used to detect lines and decide the lane that a car is supposed to take. The data for training these models are collected through sensors.

Deep learning comes in very handy when there’s a lack of domain expertise or understanding to perform feature engineering. The automatic feature extraction and end-to-end problem-solving capabilities of DL models enable them to achieve state-of-the-art results. It’s important to note that while very scalable, deep learning algorithms do require high-end hardware infrastructure for implementation.

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