The Perceptron, created by Frank Rosenblatt in the 1950s, was one of the first neural networks designed to classify patterns. Initially celebrated, it became a foundational milestone in machine learning.
Frank Rosenblatt and the Perceptron, a simple neural network machine designed to classify patterns
The human brain remains one of the greatest mysteries, far more complex than anything else we know. It is the most complicated object in the universe that we know of. The underlying processes and the source of consciousness, as well as consciousness itself, remain unknown. Neural Nets are good for popularizing Deep Learning algorithms, but we can't say for sure what mechanism behind biological Neural Networks enables intelligence to arise.
Visualized Boundaries
Weight initialization is crucial in training neural networks, as it sets the starting point for optimization algorithms. The activation function applies a non-linear transformation in our network. Different activation functions serve different purposes. Choosing the right weight initialization and activation function is key to better neural network performance. Xavier initialization is ideal for Sigmoid or Tanh in feedforward networks. He initialization pairs well with ReLU for faster convergence, especially in CNNs. Matching these improves training efficiency and model performance.
Comparison of different initialization methods
Logits are the raw outputs generated by a model before applying any activation function, such as the sigmoid or softmax functions. These values are unbounded, meaning they can be positive, negative, or zero. Logits represent the model's unnormalized confidence scores for assigning an input to different classes.
Logits vs Probabilities
In a classification problem, especially in multi-class classification, the model produces a set of logits—one for each class. These logits indicate the model's relative confidence in each class without being normalized into probabilities.
Cross-Entropy is a widely used loss function, especially in classification tasks, that measures the difference between two probability distributions.
Binary Cross-Entropy (BCE)