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Curriculum Learning With Unity ML-Agents
Curriculum Learning With Unity ML-Agents.
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Applying Curriculum Learning to Medical Images
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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Transfer Learning
As humans growing and learning in day-to-day activities right from childhood. As humans acquire knowledge by learning one task. By using the same knowledge we tend to solve the related task. Say in…
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Learning from Learned
I have just become a Ph.D student since April, so it seems a good opportunity (or maybe mandatory) for me to consider my new research direction in the Ph.D course. While thinking about the new ideas…
Read more at Becoming Human: Artificial Intelligence Magazine
All You Need to Know about In-Context Learning
“For me context is the key — from that comes the understanding of everything.” — Kenneth Noland In-context learning (ICL) is one of the most surprising model skills. Observed with GPT-3 it caught the ...
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Curriculum Learning and Symbolic Mathematics
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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Supervised Learning
As the name suggests, this type of learning algorithms needed a supervisor, while training for a particular task. Many of us apply some supervised learning algorithms unknowingly. Like It is used in…
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Reinforcement Learning
This literature review focusses on the Historic, Modern and Future developments of Reinforcement Learning.
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Reinforcement Learning
Reinforcement learning(RL) is a type of deep learning that has been receiving a lot of attention in the past few years. It is useful for the situations we want to train AI for certain skills we don’t…...
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Reinforcement Learning
In machine learning, there have main three areas. Supervised learning, unsupervised learning and reinforcement leaning. Reinforcement learning gives solutions for various kind of planning and control…...
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Meta-Learning: Learning to Learn
In order to successfully understand and study new concepts or views, people generally use one example in their commonplace. For instance, children who have seen butterflies and ants a few times can…
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Solving Machine Learning’s Generalization-Memorization Dilemma: 3 Promising Paradigms
The quest to build Machine Learning systems that learn like us Continue reading on Towards Data Science
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The Rise of Meta Learning
Meta-Learning describes the abstraction to designing higher level components associated with training Deep Neural Networks. The term “Meta-Learning” is thrown around in Deep Learning literature…
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Multitask learning: teach your AI more to make it better
Hi everyone! Today I want to tell you about the topic in machine learning that is, on one hand, very research oriented and supposed to bring machine learning algorithms to more human-like reasoning…
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Active Learning… Learning with the model
Hi everyone!! It took me some time to post but I am here with yet another exciting topic. This greatly helped me deal with the data annotation problem I was facing during my model training so I…
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How to Improve Your Network Performance by Using Curriculum Learning
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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Learning to Learn: How AI and Humans Learn
LLMs have surprised the world with their abilities. Sometimes, though, these models fail spectacularly in reasoning tasks. So there is active research to improve these models both at the architectural...
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Introduction to Adaptive Learning
Using machine learning and data science to personalize education Photo by Alexandre Van Thuan on Unsplash Data Science (DS) and Machine Learning (ML) are often leveraged to build personalised product...
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A Primer on Multi-task Learning — Part 3
Towards building a “Generalist” model. “A Primer on Multi-task Learning — Part 3” is published by Neeraj Varshney in Analytics Vidhya.
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Meta Learning: Learning to Learn in Neural Networks
This article is a survey at various learning techniques, particularly meta-learning, and their evolution over the years in the context of neural networks. Whereas conventional approaches in AI tend…
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Machines that learn by doing
In my mid-twenties I learned to play tennis for the first time. The thing about tennis is that, once you start off, it is really hard to make the ball land in the opposite side of the court (and not…
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Learning to Simulate
The paper presented at ICLR 2019 can be found here. I also have slides as well as a poster explaining the work in detail. Deep neural networks are an amazing piece of technology. With enough labelled…...
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A Primer on Multi-task Learning — Part 2
Towards building a “Generalist” model. “A Primer on Multi-task Learning — Part 2” is published by Neeraj Varshney in Analytics Vidhya.
Read more at Analytics Vidhya
Meta Learning — AI Generalised.
Deep Learning has shown immense success in various fields and is continuing to spread its wings. But one of the major issues with training any traditional neural network model is the requirement of…
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