Artificial Intelligence

Calculate accuracy score from true and predicted labels.

Implement derivatives for sigmoid and ReLU activation functions.

Implement a perceptron for binary classification with given weights [0.6, -0.4] and bias 0.3.

Calculate binary cross entropy loss between true and predicted values.

Calculate the Hamming distance between two binary strings.

Create a confusion matrix from true and predicted labels.

Implement a simple count vectorizer for text data.

Implement a decision stump (1-level decision tree).

Calculate Euclidean distance between two points.

Calculate F1 score for binary classification.

Implement feature scaling to normalize data between 0 and 1.

Create a function that implements AND logic (returns 1 only if both inputs are 1).

Create a function that implements NOT logic (returns the opposite of input).

Create a function that implements OR logic (returns 1 if either input is 1).

Calculate Jaccard similarity between two sets.

Implement K-means clustering with K=2.

Implement a simple KNN classifier with k=3.

Make predictions using a trained linear regression model (y = 2x + 1).

Make predictions using a trained logistic regression model.

Calculate Manhattan distance between two points.

Calculate mean squared error between predicted and actual values.

Implement the MiniMax algorithm for Tic-Tac-Toe.

Implement a Gaussian Naive Bayes classifier from scratch.

Convert categorical labels to one-hot encoded vectors.

Calculate precision score for binary classification.

Implement a simple Q-learning table for a grid world.

Calculate recall score for binary classification.

Implement the ReLU activation function.

Implement the sigmoid activation function.

Create a simple bag-of-words representation from text.

Implement a genetic algorithm to maximize f(x) = x^2.

Implement gradient descent to find the minimum of f(x) = x^2.

Implement a simple linear regression model (y = wx + b).

Calculate simple moving average for a time series.

Implement a perceptron for AND gate with weights [0.5, 0.5] and bias -0.7.

Implement a cosine similarity-based recommender system.

Implement the softmax function for multi-class classification.

Implement standardization using z-score normalization.

Implement a step activation function with threshold 0.

Implement a simple text tokenizer that splits on whitespace.

Train a simple MLP to solve the XOR problem.