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  • Effects of dataset size and interactions on the prediction . . .
    DL models achieved good results without interaction terms Well-specified ML models performed better than DL models Machine learning and deep learning models are very powerful in predicting the presence of a disease
  • Impact of Dataset Size on Deep Learning Model - GeeksforGeeks
    In this article, we will observe the effects of dataset size on deep learning models by focusing on a single code example that demonstrates how varying dataset sizes influence model performance Why is dataset size important? Deep learning models learn to recognize patterns by analyzing vast amounts of data
  • Why do Deep learning models need larger data sets compared . . .
    I don't think they always need large data sets, since one-shot learning algorithms can learn from a small number of examples There are two intuitive reasons that I can think of The first one is that the number of parameters that you have to train in a neural network can be huge
  • Deep learning modelling techniques: current progress . . .
    However, training DL models can be very time-consuming, expensive, and requires huge samples for better accuracy Since DL is also susceptible to deception and misclassification and tends to get stuck on local minima, improved optimization of parameters is required to create more robust models
  • Popular ML DL Models You Need to Know | by Gouranga Jha - Medium
    ML and DL models can be grouped based on how they learn from data and solve problems We will explore the different categories of machine learning and deep learning, followed by well-known
  • Explainable Deep Learning in Healthcare: A Methodological . . .
    In this review, we focus on the interpretability of the DL models in healthcare We start by introducing the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners in this eld
  • Review of Deep Learning Algorithms and Architectures | IEEE . . .
    Deep neural network (DNN) uses multiple (deep) layers of units with highly optimized algorithms and architectures This paper reviews several optimization methods to improve the accuracy of the training and to reduce training time We delve into the math behind training algorithms used in recent deep networks





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