Collecting Data for Deep Learning Development

Collecting Data for Deep Learning Development

Deep Learning development can be illustrated as a cycle of various steps, starting from data collection to ultimately deploying an app or service to users

The Deep Learning Development Lifecycle Deep Learning development can be illustrated as a cycle of various steps, starting from data collection to ultimately deploying an app or service to users. Once a Deep Learning model is deployed, monitoring its performance and how users interact with its functionality will inform what new data should be collected or what additional processing should be implemented to rebuild then redeploy a more performant model.

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How are deep learning, artificial intelligence and machine learning related

What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.