Machine Learning Concepts Explorer

Term

Select a Term

Definition

Select a term from the sidebar to see its definition.

Visualization

Relevant visualization will appear here.

/* This app will provide users with an interactive way to learn and review key concepts in machine learning. The app should present the user with a series of terms and allow them to explore related concepts and visual representations. SPECIFICATIONS: 1. The app should present the user with a central text area labeled "Term". Initially, this should display the text "Select a Term". 2. The app should feature a sidebar with a list of common Machine Learning terms. This sidebar should be scrollable if the number of items exceeds the available vertical space. The list should include at least 10 of the following terms: * Variance * Unsupervised Learning: Clustering * Time Series Analysis * Transfer Learning * Gradient Descent * Stochastic Gradient Descent (SGD) * Sentiment Analysis * Regression * Regularization * Logistic Regression * Linear Regression * Reinforcement Learning * Decision Trees * Random Forest * Truncation * Principal Component Analysis * Pre-training * Object Detection * Oversampling * Outliers * Overfitting * One-Hot Encoding * Nearest Neighbor Search * Normal Distribution * Normalization * Natural Language Processing * Matrix Factorization * Markov Chains * Model Selection * Model Evaluation * Measures of Central Tendency * Long Short-Term Memory * Jacobian Matrix * Inductive Bias * Inference in Machine Learning * Imbalanced Data * Human-in-the-Loop * Graphical Processing Unit (GPU) * Generalization in Machine Learning * Generative Adversarial Networks (GANs) * Feature Engineering * Euclidean Distance * Evolutionary Algorithms * Cost Function * Confusion Matrix * Convolutional Neural Network (CNN) * Cosine Similarity * Cross-Validation * Data Preprocessing * Decision Trees * Dense Vectors * Dropout * Bagging * Tanh Function * ReLU Function * Root Mean Square Error * R-Squared * Recurrent Neural Network (RNN) * Quantum Machine Learning 3. When a user clicks on a term in the sidebar, the "Term" text area should update with the selected term. In addition, another text area below the "Term" area, labeled "Definition", should display a concise definition of the selected term. 4. Below the "Definition" area, include an element for a visual representation, labeled "Visualization". This could be an image (e.g., a diagram or chart) or a simple animation (e.g., using SVG). The visualization should be relevant to the selected term and aid in understanding its meaning. For example: * For "Gradient Descent," the visualization could show a curve with a point iteratively descending toward a minimum. * For "Decision Trees," the visualization could show a branching tree diagram representing the decision-making process. * For "Clustering," the visualization could show a scatter plot with data points grouped into clusters. * For “Convolutional Neural Networks”, the visualization could show an image being processed through layers of filters. 5. Keep the app simple and visually clean. The focus should be on clarity and ease of use. Use a consistent color scheme and clear typography. 6. All styles and scripts should be inline for simplicity. The app must be fully responsive and function properly on both desktop and mobile. Provide the code as a single, self-contained HTML document. All styles and scripts must be inline. In the result, encase the code between "```" and "```" for easy parsing. Edit edit */