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And this is particularly true for accounts payable (AP) programs, where AI, coupled with advancements in deep learning, computer vision and natural language processing (NLP), is helping drive increased efficiency, accuracy and cost savings for businesses. Answering them, he explained, requires an interdisciplinary approach.
For instance, many blogs today feature AI-generated text powered by LLMs (Large Language Modules) like ChatGPT or GPT-4. Many data sources contain AI-generated images created using DALL-E2 or Midjourney. Moreover, AIresearchers are using synthetic data generated using Generative AI in their model training pipelines.
Improve model accuracy: In-depth feature engineering (example, PCA) Hyperparameter optimization (HPO) Quality assurance and validation with test data. Monitoring setup (model, datadrift). Data Engineering Explore using feature store for future ML use cases. He has a background in software engineering and AIresearch.
If this in-depth educational content is useful for you, you can subscribe to our AIresearch mailing list to be alerted when we release new material. Data Any machine learning endeavour starts with data, so we will start by clarifying the structure of the input and target data that are used during training and prediction.
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