architecture

Demystifying Overfitting in Deep Neural Networks: Separating Fact from Fiction

Overfitting is often perceived as a major challenge in DNNs, leading to a lack of confidence in their ability to generalize to new data. As Neal Shusterman, the author of "Unwind", once wrote: “But remember that good intentions pave many roads. Not all of them lead to hell.“ However, the reality is that the severity of overfitting in DNNs is often overstated and can be effectively mitigated through various techniques.....

Debugging Deep Learning Models: Strategies and Best Practices

Debugging deep learning models is a complex and challenging task that requires significant expertise and experience. One of the main reasons for this complexity is that deep learning models involve multiple layers of interconnected nodes, which can make it difficult to pinpoint the source of errors or identify areas where optimization is needed....