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The ways to avoid Gradient Vanishing and Exploding-Weight Initialization, Xavier, He, Batch Normalization, Internal Covariate Shift, Layer Normalization

Weight Initialization The aim of weight initialization is to prevent layer action outputs from exploding or vanishing during the course of a forward pass through a deep neural network. If either occurs, loss gradients will either be too large or too small to flow backwards beneficially, and the network will take longer to converge.1. Xavier Initialization It initializes the weights in your netwo..

eq, view_as

eq It stands for "equal" - it performs element-wise equality comparison between tensors.tensor1.eq(tensor2) # Returns True/False for each elementCompares each element in tensor1 with corresponding element in tensor2Returns a boolean tensor with True where elements are equal, False otherwiseFor example, predicted = torch.tensor([1, 2, 3, 4, 5])targets = torch.tensor([1, 0, 3, 0, 5])result = pre..

float16 VS float32 VS float64, int8 VS int16 VS int32 VS int64

float16 VS float32 VS float64 These are different precision levels for floating-point numbers, defined by how many bits are used to store them: Precision levels:float16 (half precision): 16 bitsRange: ±65,504Precision: ~3-4 decimal digitsUse: Memory-constrained models, mixed precision trainingfloat32 (single precision): 32 bitsRange: ±3.4 × 10³⁸Precision: ~7 decimal digitsUse: Default for most P..