While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions.
<coherence> While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions.
While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", as it can be difficult to discover what input information is used to make predictions.