Activation functions are what make neural networks more than just glorified linear regressions. They add non-linearity — which gives neural networks their superpower: the ability to learn complex patterns and behaviors. Their derivatives are critical for training the network effectively through backpropagation.
🤖 What Are Activation Functions?
These three terms are often used interchangeably, but they refer to different concepts with varying levels of complexity and capability.
🧠What is a Computation Graph?
🟢 1. One-Hot Encoding
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🧠Overview
🧠Overview
What Is the Drivetrain Approach?
Training a Document Classifier
🟢 Forward Propagation (Forward Pass)
You’ve learned how to do forward propagation, backpropagation, and gradient descent. But now comes a crucial design decision: How do we initialize the weights?
In machine learning, weights are crucial parameters that are learned during the training of a model, such as a neural network. They help the model make predictions by assigning importance to the inputs it receives. Let's break down the concept further:
Understanding Neural Networks, Weights, and Stochastic Gradient Descent