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Generative pretrained transformer (GPT) – AI tech & AI learning models

Sascha Block2024-04-01T22:39:39+02:00Tags: AI, AI, Alan Turing, AlexNet, Backpropagation, Context limitations, Dartmouth Conference, Deep Blue, Deep learning, Distortions, Embeddings, Fine tuning, Generative Pretrained Transformer, GPT, GPT-2, GPT-3, ImageNet, machine learning, neural networks, neural networks, OpenAI, Positional Encoding, Self-Attention, Text generation, Tokenization, Top-k Sampling, Transformer architecture, Turing test, unsupervised learning, Unsupervised Learning|

What is a generative pretrained transformer (GPT)? A Generative Pre-Trained Transform - GPT for short - is an advanced machine learning AI model based on the Transformer architecture. This AI technology was developed by OpenAI and is known for its ability to generate high-quality texts that are similar to those of a human being. The core of GPT technology lies in the use of large amounts of data to pre-train the model before it is fine-tuned for specific tasks.

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Genetic algorithms

Sascha Block2023-12-08T12:10:16+01:00Tags: Adaptability in AI, AI decision making, AI development, AI modeling, algorithmic innovations, algorithmic models, algorithmic pattern recognition, Algorithms in AI, Artificial intelligence, Automation in AI, Climate change prediction, Data-driven optimization, digital maps, Ecological data analysis, Effects of climate change, Environmental informatics, Environmental management, Environmental planning, Environmental strategies, evolutionary algorithms, Feature selection, genetic algorithm, Genetic programming, Hyperparameter tuning, Learning algorithms, machine learning, machine learning training, Nature conservation, neural networks, Optimization, Optimization algorithms, Resource management, Simulation, Sustainable decision-making, Sustainable use of resources|

Genetic algorithms are a class of optimization methods based on the principles of natural evolution and genetics. A genetic algorithm is ideal for finding solutions to complex problems by mimicking evolutionary processes such as selection, crossover and mutation. In the field of machine learning, genetic algorithms offer a unique and effective approach, especially when it comes to optimizing learning models and selecting features.

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