How can you design an ANN to handle missing or noisy data?
Missing or noisy data is a common challenge in machine learning, especially when working with real-world datasets. It can affect the performance, accuracy, and reliability of artificial neural networks (ANNs), which are powerful models that can learn from complex patterns and nonlinear relationships. How can you design an ANN to handle missing or noisy data? In this article, you will learn some strategies and techniques that can help you deal with this problem and improve your ANN results.
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Sagar MoreTop LinkedIn Voice | Digital Transformation Leader | DevOps, AIOps & SRE Architect | Cloud & Edge Innovator |…
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Dr. Priyanka Singh Ph.D.?? AI Author ?? Transforming Generative AI ?? Responsible AI - Lead MLOps @ Universal AI ?? Championing AI Ethics &…
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Hasan AsifMachine Learning Engineer | Python, SQL, NLP/LLM, BERT, ETL, PySpark, AWS Expert