https://www.selleckchem.com/products/mk-5108-vx-689.html Neural networks perform significantly better than common linear approaches in the given task, in particular when sufficiently large architectures are used. This can be explained by salient properties of the underlying data, and by theoretical and experimental analysis of the neural network mapping. To the best of our knowledge, this work is the first one in the field which not only reports that large and deep neural networks are superior to existing architectures, but also explains this result. To the best of our knowledge, this work is the first one in the field which not only reports that large and deep neural networks are superior to existing architectures, but also explains this result. The rise in long-term antidepressant use is concerning. Long-term antidepressant (AD) use, much longer than recommended by guidelines, can result in risk of adverse events and generate unnecessary costs. In order to mitigate these risks, patients views about their antidepressants and how to discontinue need to be taken into account. We aimed to explore patients' experiences and views of discontinuing long-term AD, barriers and facilitators of discontinuation and required support. Semi-structured face to face interviews were conducted with 14 patients with long-term AD use in primary care. Interviews were analysed thematically. Participants describe various perceptions about discontinuation. There is fear of returning to their depression, even in those who were ambivalent about the effectiveness and safety of AD continuation. Participants describe low confidence in their own coping resources, fear of stress, and previous negative experiences with stopping. This enhances their perception of AD dependence. Participants indicate the importance of the support of their GP and their social network to help them withdraw. Discontinuation of long-term antidepressants is a complex issue for patients. More awareness of the lack of evid