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E&ICT Academy, IIT Kanpur

年1月21日

Can you guess which activation function is best for handling vanishing gradient issues? Drop your answer! #AI #DeepLearningTips

eicta_iitk's tweet image. Can you guess which activation function is best for handling vanishing gradient issues? Drop your answer!

#AI #DeepLearningTips

Techfuturism

@Techfuturism2

年11月19日

Mastering Learning Rate Machine Learning: A Powerful Guide for Positive Results techfuturism.com/mastering-lear… #DeepLearningTips, #LearningRate, #MachineLearningBasics, #MLTraining

Techfuturism2's tweet image. Mastering Learning Rate Machine Learning: A Powerful Guide for Positive Results techfuturism.com/mastering-lear…
#DeepLearningTips, #LearningRate, #MachineLearningBasics, #MLTraining

💡 ML Snippet of the Day: Have you tried 'Gradient Clipping'? This technique keeps gradients from exploding, helping models train more smoothly! Useful in deep learning to avoid instability issues. 🚀 #MachineLearning #DeepLearningTips

Pranay Suyash

@pranaysuyash

2023年8月11日

Why is prompt engineering crucial? 🤔 It helps in maximizing the utility of a model, especially in zero-shot or few-shot scenarios. The better the prompt, the better the result! #DeepLearningTips

Christy Varghese

@technomadlyf

2024年11月12日

3. Non-Zero-Centered Output Sigmoid outputs only positive values, which can create unbalanced gradients. During backprop, this can lead to zig-zagging in gradient updates, making training slower. Alternative functions like ReLU can avoid this. #DeepLearningTips #Sigmoid

vizmook

@vizmook

2018年1月8日

Hacking on an instance segmentation deep learning project? Check out the Cityscapes and COCO datasets. #DeepLearningTips

Jesus Ruiz Dev | 17

@JesusRuizDev97

年10月11日

Beyond backprop: Weight mirroring at initialization. Forces rapid convergence, avoids local minima. Few understand the power of symmetry. #AISymmetry #ModelAlchemy #DeepLearningTips