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Curriculum-Learning

Curriculum Learning is an educational approach inspired by human learning processes, where tasks are presented in a structured order based on their difficulty. This method begins with simpler examples, gradually progressing to more complex ones, allowing learners—whether humans or artificial intelligence systems—to build foundational knowledge before tackling challenging problems. The concept, proposed by Elman in 1993 and later supported by Bengio in 2009, has been shown to enhance performance in various domains, including deep learning and reinforcement learning. By mimicking the way humans learn, Curriculum Learning aims to improve efficiency and effectiveness in training models across different applications.

How to Improve Your Network Performance by Using Curriculum Learning

 Towards Data Science

Curriculum learning describes a type of learning in which you first start out with only easy examples of a task and then gradually increase the task difficulty. We humans have been learning according…...

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Applying Curriculum Learning to Medical Images

 Towards Data Science

General Overview. In this study, I worked with a team of researchers to apply curriculum learning to improve the accuracy of a deep learning model for classifying colorectal cancer images. The full…

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Curriculum Learning With Unity ML-Agents

 Towards Data Science

Curriculum Learning With Unity ML-Agents.

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Curriculum Learning and Symbolic Mathematics

 Towards AI

We can think of the humongous field of deep learning as the Earth’s crust, floating on a mantle of mathematical and algorithmic understanding. It is a vast sphere of knowledge that is divided into…

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Learning Engineering: Merging Science and Data to Design Powerful Learning Experiences

 Towards Data Science

Learning is an organic process where the seeds of knowledge are slowly and steadily been planted and engraved into the minds. This seeds of knowledge eventually bear fruit when it is put into action…

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Plotting Learning Curves

 Scikit-learn Examples

Plotting Learning Curves In the first column, first row the learning curve of a naive Bayes classifier is shown for the digits dataset. Note that the training score and the cross-validation score are ...

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Active Learning Behind The Scenes

 Towards Data Science

In my previous posts, I described what is Active Learning (introduction to Active Learning and the main approaches) and how one can implement such a pipeline in a simple and generic way (architecture…...

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Systematic Learning Matters

 Towards Data Science

During the first 20 years of my life, I was a passive learner. Schools and college had been my major knowledge source: Every semester, faculty members provided a syllabus with studying plans for each…...

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Gentle Introduction to Knowledge Representation Learning

 Towards Data Science

Knowledge representation learning (KRL) mainly focus on the process of learning knowledge graph embeddings, while keeping the semantic similarities. This has proven extremely useful, as feature…

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Q-Learning

 Towards Data Science

Welcome to my column on reinforcement learning, where I spend some time going over some very interesting concepts revolving around the nature of learning with a computational approach. As with most…

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Learn By Sharing

 Towards Data Science

Traditional education is simple: sit down, shut up, and listen to the teacher. After class, go to the library to repeatedly read the same words, trying to figure out abstract topics with little…

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Continual Learning: A Primer

 Towards Data Science

Plus paper recommendations Continue reading on Towards Data Science

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