Upload corrected train split dataset layout

README.md

@@ -8,10 +8,10 @@ It has 15 educational diagram images and metadata that can be used to build a Pi
 
 ```text
 data/sample_knowledge_base/
-  images/
-    *.png
-  metadata.csv
-  metadata.jsonl
+  train/
+    images/
+      *.png
+    metadata.csv
   README.md
 ```
 
@@ -85,7 +85,7 @@ dataset = load_dataset(
 print(dataset[0])
 ```
 
-The dataset should include an `image` column plus the metadata fields from `metadata.jsonl`.
+The dataset should include an `image` column plus the metadata fields from `metadata.csv`.
 
 ## Dataset Topics
 
@@ -106,4 +106,3 @@ The 15 included images are:
 13. Naive Bayes - Prior, Likelihood, Posterior
 14. Convolutional Neural Networks - CNN Convolution
 15. Transformers - Attention Mechanism
-

train/metadata.csv

@@ -0,0 +1,16 @@
+id,file_name,image_path,topic,title,description,search_queries,embedding_text
+linear_regression_best_fit,images/linear_regression_best_fit.png,images/linear_regression_best_fit.png,Linear Regression,Line of Best Fit,A scatter plot showing data points and a straight best-fit line used in linear regression.,"linear regression, line of best fit, scatter plot, prediction, supervised learning","Linear Regression. Line of Best Fit. A scatter plot showing data points and a straight best-fit line used in linear regression. Keywords: linear regression, line of best fit, scatter plot, prediction, supervised learning."
+support_vector_machine_margin,images/support_vector_machine_margin.png,images/support_vector_machine_margin.png,Support Vector Machines,SVM Margin,"A classification diagram showing two classes, a decision boundary, and the maximum margin in a support vector machine.","support vector machine, SVM, margin, decision boundary, classification","Support Vector Machines. SVM Margin. A classification diagram showing two classes, a decision boundary, and the maximum margin in a support vector machine. Keywords: support vector machine, SVM, margin, decision boundary, classification."
+pca_components,images/pca_components.png,images/pca_components.png,Principal Component Analysis,PCA Components,A two-dimensional point cloud with first and second principal component axes showing dimensionality reduction.,"PCA, principal component analysis, dimensionality reduction, eigenvectors, components","Principal Component Analysis. PCA Components. A two-dimensional point cloud with first and second principal component axes showing dimensionality reduction. Keywords: PCA, principal component analysis, dimensionality reduction, eigenvectors, components."
+decision_tree_split,images/decision_tree_split.png,images/decision_tree_split.png,Decision Trees,Decision Tree Split,A simple decision tree showing how features split data into branches and leaf predictions.,"decision tree, feature split, branches, leaves, classification tree","Decision Trees. Decision Tree Split. A simple decision tree showing how features split data into branches and leaf predictions. Keywords: decision tree, feature split, branches, leaves, classification tree."
+neural_network_layers,images/neural_network_layers.png,images/neural_network_layers.png,Neural Networks,Neural Network Layers,"A neural network diagram with input neurons, hidden layers, weighted connections, and output neurons.","neural network, input layer, hidden layer, output layer, deep learning","Neural Networks. Neural Network Layers. A neural network diagram with input neurons, hidden layers, weighted connections, and output neurons. Keywords: neural network, input layer, hidden layer, output layer, deep learning."
+gradient_descent_steps,images/gradient_descent_steps.png,images/gradient_descent_steps.png,Gradient Descent,Gradient Descent,A loss curve showing gradient descent steps moving downhill toward the minimum loss.,"gradient descent, loss function, optimization, learning rate, minimum","Gradient Descent. Gradient Descent. A loss curve showing gradient descent steps moving downhill toward the minimum loss. Keywords: gradient descent, loss function, optimization, learning rate, minimum."
+confusion_matrix,images/confusion_matrix.png,images/confusion_matrix.png,Model Evaluation,Confusion Matrix,"A confusion matrix explaining true positives, false positives, false negatives, and true negatives.","confusion matrix, true positive, false positive, recall, precision","Model Evaluation. Confusion Matrix. A confusion matrix explaining true positives, false positives, false negatives, and true negatives. Keywords: confusion matrix, true positive, false positive, recall, precision."
+knn_neighbors,images/knn_neighbors.png,images/knn_neighbors.png,K-Nearest Neighbors,KNN Classification,A K-nearest neighbors diagram showing a query point classified by nearby labeled points.,"KNN, k nearest neighbors, query point, classification, distance","K-Nearest Neighbors. KNN Classification. A K-nearest neighbors diagram showing a query point classified by nearby labeled points. Keywords: KNN, k nearest neighbors, query point, classification, distance."
+clustering_kmeans,images/clustering_kmeans.png,images/clustering_kmeans.png,K-Means Clustering,K-Means Clusters,A clustering diagram showing three groups of points and their centroids in k-means clustering.,"k-means, clustering, centroid, unsupervised learning, clusters","K-Means Clustering. K-Means Clusters. A clustering diagram showing three groups of points and their centroids in k-means clustering. Keywords: k-means, clustering, centroid, unsupervised learning, clusters."
+dataset_split_workflow,images/dataset_split_workflow.png,images/dataset_split_workflow.png,Machine Learning Workflow,Train Test Split,A dataset split diagram showing training data used to fit a model and test data used for evaluation.,"train test split, model validation, training data, testing data, machine learning workflow","Machine Learning Workflow. Train Test Split. A dataset split diagram showing training data used to fit a model and test data used for evaluation. Keywords: train test split, model validation, training data, testing data, machine learning workflow."
+overfitting_underfitting,images/overfitting_underfitting.png,images/overfitting_underfitting.png,Model Generalization,Overfitting vs Underfitting,"A comparison of underfitting, good fit, and overfitting curves on the same kind of data.","overfitting, underfitting, good fit, bias variance, model generalization","Model Generalization. Overfitting vs Underfitting. A comparison of underfitting, good fit, and overfitting curves on the same kind of data. Keywords: overfitting, underfitting, good fit, bias variance, model generalization."
+roc_curve,images/roc_curve.png,images/roc_curve.png,Model Evaluation,ROC Curve,An ROC curve showing true positive rate versus false positive rate with an AUC label.,"ROC curve, AUC, true positive rate, false positive rate, classifier evaluation","Model Evaluation. ROC Curve. An ROC curve showing true positive rate versus false positive rate with an AUC label. Keywords: ROC curve, AUC, true positive rate, false positive rate, classifier evaluation."
+naive_bayes,images/naive_bayes.png,images/naive_bayes.png,Naive Bayes,Naive Bayes,A probability diagram showing how Naive Bayes combines prior and likelihood evidence to classify a sample.,"naive bayes, probability, prior, likelihood, posterior, classification","Naive Bayes. Naive Bayes. A probability diagram showing how Naive Bayes combines prior and likelihood evidence to classify a sample. Keywords: naive bayes, probability, prior, likelihood, posterior, classification."
+cnn_convolution,images/cnn_convolution.png,images/cnn_convolution.png,Convolutional Neural Networks,CNN Convolution,"A convolutional neural network diagram showing an image, a sliding filter, a feature map, and classification.","CNN, convolution, filter, feature map, image classification","Convolutional Neural Networks. CNN Convolution. A convolutional neural network diagram showing an image, a sliding filter, a feature map, and classification. Keywords: CNN, convolution, filter, feature map, image classification."
+attention_mechanism,images/attention_mechanism.png,images/attention_mechanism.png,Transformers,Attention Mechanism,"A transformer attention diagram showing query, key, value vectors and weighted attention scores.","attention mechanism, transformer, query key value, self attention, weights","Transformers. Attention Mechanism. A transformer attention diagram showing query, key, value vectors and weighted attention scores. Keywords: attention mechanism, transformer, query key value, self attention, weights."