The vanishing gradient problem occurs when gradients of the loss function become too small during backpropagation, making it difficult for neural networks to learn and update weights in earlier layers. This issue is particularly prevalent in deep networks with activation functions like sigmoid or tanh, leading to slow convergence or complete stagnation of training.

Vanishing Gradient Problem

vanishing gradient problem

gradients of the loss function

Backpropagation is a fundamental algorithm in training neural networks, allowing the network to learn by minimizing the error between predicted and actual outputs through the iterative adjustment of weights. It efficiently computes the gradient of the loss function with respect to each weight by applying the chain rule of calculus, enabling the use of gradient descent optimization techniques.

backpropagation

Neural networks are computational models inspired by the human brain, designed to recognize patterns and solve complex problems by learning from data. They consist of interconnected layers of nodes, or 'neurons', that process input data and iteratively adjust their weights to minimize error and improve accuracy.

neural networks

update weights

earlier layers

deep networks

Activation functions are mathematical equations that determine the output of a neural network model by introducing non-linearity, allowing the network to learn complex patterns. They are crucial for the backpropagation process because they enable the computation of gradients, which are essential for updating the model's weights during training.

activation functions

sigmoid

The hyperbolic tangent function, or tanh, is an activation function used in neural networks that outputs values between -1 and 1, providing a zero-centered distribution which can help in faster convergence during training. It is particularly useful for handling negative inputs and gradients, reducing the likelihood of issues such as the vanishing gradient problem compared to the sigmoid function.

Relevant Degrees

Log in to see lessons