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AI engineering is the discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts, which combines the principles of systems engineering, software engineering, and computerscience to create AI systems.
Deep Learning has been used to achieve state-of-the-art results in a variety of tasks, including image recognition, NaturalLanguageProcessing, and speech recognition. NaturalLanguageProcessing (NLP) This is a field of computerscience that deals with the interaction between computers and human language.
Model invocation We use Anthropics Claude 3 Sonnet model for the naturallanguageprocessing task. This LLM model has a context window of 200,000 tokens, enabling it to manage different languages and retrieve highly accurate answers. temperature This parameter controls the randomness of the language models output.
Tech companies, they might focus on developing recommendation systems, fraud detection algorithms, or NaturalLanguageProcessing tools. Most professionals in this field start with a bachelor’s degree in computerscience, Data Science, mathematics, or a related discipline. Platforms like Pickl.AI
Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. We perform a five-fold cross-validation to select the best model during training, and perform hyperparameter optimization to select the best settings on multiple model architecture and training parameters.
Artificial Intelligence (AI): A branch of computerscience focused on creating systems that can perform tasks typically requiring human intelligence. Cross-Validation: A model evaluation technique that assesses how well a model will generalise to an independent dataset.
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