The strength of certain neural connections can predict how well someone can learn math, and mildly electrically stimulating ...

This important study demonstrates the significance of incorporating biological constraints in training neural networks to develop models that make accurate predictions under novel conditions. By ...

Confused about cost functions in neural networks? In this video, we break down what cost functions are, why they matter, and which types are best for different applications—from classification ...

To mitigate this issue, the proposed method utilized heuristics like outlier suppression and robust loss functions (e.g., Huber loss) to improve convergence and generalization. For instance, the Huber ...

This paper investigates a class of coupled neural networks with delays and ad-dresses the exponential synchronization problem using delay-compensatory impulsive control. Razumikhin-type inequalities ...

Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of ...

Abstract: Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the ...

pyplot.legend() pyplot.show() Conclusion In this blog, we have covered most of the loss functions that are used in deep learning for regression and classification problem. Further, we can experiment ...