Posted inTechnology Insights
Fonction d’activation neuronale
Softmax softmax(xᵢ) = eˣⁱ / ∑eˣʲ Used for multi-class classification. Outputs probability distribution.
Posted inTechnology Insights
Fonction d’activation neuronale
ReLU ReLU(x) = max(0, x) Most popular. Computationally efficient, helps with vanishing gradient problem.
Posted inTechnology Insights
Fonction d’activation neuronale
Tanh tanh(x) = (eˣ - e⁻ˣ) / (eˣ + e⁻ˣ) Output range: (-1, 1). Zero-centered, often better than sigmoid.
Posted inTechnology Insights
Fonction d’activation neuronale
Sigmoid σ(x) = 1 / (1 + e⁻ˣ) Output range: (0, 1). Used for binary classification and probability outputs
Posted inDigital Storytelling Web Development
Bayes theorem
the guy who developed Bayesian stats as it is today is a French mathematician called Pierre-Simon Laplace on peut se référer aux travaux de Pierre-Simon Laplace tels qu’ils sont présentés…
Posted inTechnology Insights Web Development
Free Solution in Chat. Yeah, both work well for sure.
But honestly, for getting restarted with development, the free chat + VS Code combo is solid and gets you far.
