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Deep Residual Learning for Image Recognition (ResNet Explained)

Analytics Vidhya

One of the key breakthroughs in deep learning is the ResNet architecture, introduced in 2015 by Microsoft Research. Introduction Deep learning has revolutionized computer vision and paved the way for numerous breakthroughs in the last few years.

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Algorithmic biases – Is it a challenge to achieve fairness in AI?

Data Science Dojo

Just like people, Algorithmic biases can occur sometimes. AI algorithms are used to make decisions about everything from who gets a loan to what ads we see online. However, AI algorithms can be biased, which can have a negative impact on people’s lives. Thinking why? Well, think of AI as making those characters.

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Incremental Garbage Collection in Ruby 2.2 (2015)

Hacker News

We call this algorithm RincGC. This article introduces incremental garbage collection (GC) which has been introduced in Ruby 2.2. RincGC achieves short.

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Route Planning in Transportation Networks (2015)

Hacker News

We survey recent advances in algorithms for route planning in transportation networks. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale.

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LLM-Powered Metadata Extraction Algorithm

Towards AI

The evolution of Large Language Models (LLMs) allowed for the next level of understanding and information extraction that classical NLP algorithms struggle with. Advances in Neural Information Processing Systems 28 (NIPS 2015). But often, these methods fail on more complex tasks.

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Counting shots, making strides: Zero, one and few-shot learning unleashed 

Data Science Dojo

In this approach, the algorithm learns patterns and relationships between input features and corresponding output labels. Traditional learning approaches Traditional machine learning predominantly relied on supervised learning, a process where models were trained using labeled datasets.

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Frustrated by Creating Test Data?

Towards AI

From 2005 to 2015, I taught data science classes to groups within corporations. But the task of teaching data, syntax, algorithms, and applications within 1–3 days was daunting. I believe that I have two key differentiators in “Making AI & ML Accessible to All.” I was lucky that my participants were bright and motivated.