Remove 2018 Remove Machine Learning Remove Supervised Learning
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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. This approach involves techniques where the machine learns from massive amounts of data.

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DeepMind

Dataconomy

Technology and methodology DeepMind’s approach revolves around sophisticated machine learning methods that enable AI to interact with its environment and learn from experience. AlphaGo Zero: This iteration used unsupervised reinforcement learning, allowing the program to exceed its predecessors consistently.

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The Hidden Cost of Poor Training Data in Machine Learning: Why Quality Matters

How to Learn Machine Learning

The quality of your training data in Machine Learning (ML) can make or break your entire project. This article explores real-world cases where poor-quality data led to model failures, and what we can learn from these experiences. Machine learning algorithms rely heavily on the data they are trained on.

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Are AI technologies ready for the real world?

Dataconomy

AI has made significant contributions to various aspects of our lives in the last five years ( Image credit ) How do AI technologies learn from the data we provide? AI technologies learn from the data we provide through a structured process known as training. Another form of machine learning algorithm is known as unsupervised learning.

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What a data scientist should know about machine learning kernels?

Mlearning.ai

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Support Vector Machine Support Vector Machine ( SVM ) is a supervised learning algorithm used for classification and regression analysis.

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Modern NLP: A Detailed Overview. Part 2: GPTs

Towards AI

Year and work published Generative Pre-trained Transformer (GPT) In 2018, OpenAI introduced GPT, which has shown, with the implementation of pre-training, transfer learning, and proper fine-tuning, transformers can achieve state-of-the-art performance. But, the question is, how did all these concepts come together?

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Against LLM maximalism

Explosion

Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find. That’s not a path to improvement.