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Artificial Neuron: A perceptron is an artificial neuron or a simplified model of a biological neuron, used as a building block for artificial neural networks. Binary Classification: Perceptrons are primarily used for binary classification tasks, where the goal is to categorize input data into one of two possible classes or categories. Linear Model: It is a linear model that takes a weighted sum ..

Cross-Entropy (CE) loss: A cost function commonly used to evaluate the performance of classification models, particularly for probabilistic outputs. Calculation: CE loss measures the dissimilarity between the predicted probability distribution (y_pred) and the true probability distribution (y_true) for each class. Probabilistic Models: Particularly suitable for models that output probabilities, ..

MSE(Mean Squared Error) Error Metric: It is a commonly used error metric in regression problems. Calculation: MSE is calculated by taking the average of the squared differences between the predicted values and the true values. Objective: The main goal in minimizing MSE is to improve the model's accuracy by reducing the squared errors. Sensitivity: MSE is more sensitive to outliers, as it squares..

This is a simple table for comparison between classification and regression, as it occurs continuously in ML study. to sum it up, Classification predicts categorical labels, while regression predicts continuous values in machine learning tasks.

Examples of a extension ) Email -> Spam/Non-Spam Price -> Low/High Tumor -> Malignant/Benign Threshold - a value that is used to make a binary decision based on a continuous value. - It is commonly used in binary classification problems, where the output of the model is a probability score between 0 and 1, and the threshold is used to determine whether the input belongs to one of two classes. - ..

Learning Rate : a hyperparameter that controls how much the model adjusts its parameters in response to the error gradient during training. If the learning rate is too low, it takes too much time in optimization, while when it is too high, it has difficulty in converging itself.