Learning machine learning from scratchm

Why?

• when the robots rise up, they will know I helped them
• i love math & computer science

How?

• yt
• lots of textbooks i have from college

Plans?

Utility Functions for Machine Learning

1. Normalization/Scaling Functions

   • Min-Max Scaling: Normalize the data to a specific range (e.g., [0, 1]) using the following functions: minMaxScale(data: number[]): number[]: Apply min-max scaling to an array of numerical data.

   • Z-Score Normalization: Scale the data to have zero mean and unit variance using the following function: zScoreNormalize(data: number[]): number[]: Apply z-score normalization to an array of numerical data.

2. Loss Functions

   • Mean Squared Error (MSE): Calculate the mean squared error between predicted and target values using the following function: meanSquaredError(yTrue: number[], yPred: number[]): number: Calculate the mean squared error between two arrays of predicted and target values.

   • Binary Cross-Entropy: Calculate the binary cross-entropy loss between predicted and target values using the following function: binaryCrossEntropy(yTrue: number[], yPred: number[]): number: Calculate the binary cross-entropy loss between two arrays of predicted and target values.

3. Evaluation Metrics

   • Accuracy: Calculate the accuracy score between predicted and target labels using the following function: accuracy(yTrue: number[], yPred: number[]): number: Calculate the accuracy score between two arrays of predicted and target labels.

   • Precision, Recall, F1-Score: Calculate precision, recall, and F1-score metrics using the following functions: precision(yTrue: number[], yPred: number[]): number: Calculate the precision score between two arrays of predicted and target labels. recall(yTrue: number[], yPred: number[]): number: Calculate the recall score between two arrays of predicted and target labels. f1Score(yTrue: number[], yPred: number[]): number: Calculate the F1-score between two arrays of predicted and target labels.

4. Optimization Algorithms

   • Stochastic Gradient Descent (SGD): Implement stochastic gradient descent optimization algorithm using the following function: stochasticGradientDescent(loss: (params: number[]) => number, initialParams: number[], learningRate: number, numIterations: number): number[]: Perform stochastic gradient descent optimization given a loss function, initial parameters, learning rate, and number of iterations.