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A Non-Deep Learning Approach to Computer Vision

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A World of Computer Vision Outside of Deep Learning Photo by Museums Victoria on Unsplash IBM defines computer vision as “a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs [1].”

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Machine Learning vs. Deep Learning - A Comparison

Heartbeat

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Deep Learning? This is why the technique is known as "deep" learning.

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The Deep of Deep Learning

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Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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Using Deep Learning To Improve the Traditional Machine Learning Performance

Heartbeat

Deep learning for feature extraction, ensemble models, and more Photo by DeepMind on Unsplash The advent of deep learning has been a game-changer in machine learning, paving the way for the creation of complex models capable of feats previously thought impossible.

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Top 8 Machine Learning Algorithms

Data Science Dojo

Support Vector Machines (SVM): This algorithm finds a hyperplane that best separates data points of different classes in high-dimensional space. Anomaly Detection Anomaly detection, like noticing a misspelled word in an essay, equips machine learning models to identify data points that deviate significantly from the norm.

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AI Drug Discovery: How It’s Changing the Game

Becoming Human

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed.

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From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

Three significant events affected the evolution of these models. The earlier models that were SOTA for NLP mainly fell under the traditional machine learning algorithms. These included the Support vector machine (SVM) based models.