Dave's Ideas on Grokking for Openai/Google/Baidu

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python script exploring grokking concepts and further gnerzliation techniques without model training, 50 ideas

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Certainly! Below are 50 ideas for Python scripts that explore the concepts of grokking, memorization, generalization, and related techniques in machine learning. These ideas range from basic explorations to more advanced concepts, and they can be used without the need for model training.

Visualizing XOR Problem: Create a script to visualize the XOR problem, a classic example of non-linear separability.

Simulating Weight Decay: Write a script to simulate the effect of weight decay on a simple linear model.

Exploring Activation Functions: Create a script to plot different activation functions like ReLU, Sigmoid, and Tanh.

Circular Embedding Visualization: Write a script to visualize circular embeddings, as mentioned in the modular arithmetic problem.

Discrete Fourier Transform (DFT) Exploration: Create a script to apply the DFT to a sequence and visualize the result.

Bias-Variance Tradeoff Simulation: Write a script to simulate the bias-variance tradeoff in a simple regression model.

Dropout Simulation: Create a script to simulate the effect of dropout in a neural network.

L1 vs L2 Regularization Comparison: Write a script to compare the effects of L1 and L2 regularization.

Data Augmentation Techniques: Create a script to apply and visualize different data augmentation techniques.

Sensitivity to Hyperparameters: Write a script to explore how a model's performance changes with different hyperparameters.

Ensemble Learning Techniques: Create a script to demonstrate basic ensemble learning techniques like bagging and boosting.

Cross-Validation Techniques: Write a script to implement and visualize different cross-validation techniques.

Feature Selection Techniques: Create a script to explore different feature selection techniques like mutual information.

Principal Component Analysis (PCA) Visualization: Write a script to apply PCA to a dataset and visualize the result.

t-SNE Visualization: Create a script to apply t-SNE to a dataset for dimensionality reduction and visualization.

K-Fold Cross-Validation: Write a script to implement k-fold cross-validation on a dataset.

Grid Search Hyperparameter Tuning: Create a script to perform grid search for hyperparameter tuning.

Random Search Hyperparameter Tuning: Write a script to perform random search for hyperparameter tuning.

Simulating Overfitting and Underfitting: Create a script to simulate overfitting and underfitting in a model.

Exploring Learning Rate Schedules: Write a script to explore different learning rate schedules like exponential decay.

Batch Normalization Simulation: Create a script to simulate the effect of batch normalization in a neural network.

Early Stopping Implementation: Write a script to implement early stopping in training a model.

Visualizing Decision Boundaries: Create a script to visualize decision boundaries for different classifiers.

Simulating Noise in Data: Write a script to add different types of noise to a dataset and analyze the effects.

Implementing a Simple Genetic Algorithm: Create a script to implement a simple genetic algorithm for optimization.

Simulating Data Imbalance: Write a script to simulate and handle data imbalance in a classification problem.

Exploring Different Distance Metrics: Create a script to explore different distance metrics like Euclidean, Manhattan, etc.

Visualizing Confusion Matrices: Write a script to visualize confusion matrices for classification problems.

ROC Curve and AUC Visualization: Create a script to plot ROC curves and calculate AUC for different classifiers.

Implementing a Simple K-Nearest Neighbors (K-NN) Algorithm: Write a script to implement a simple K-NN algorithm.

Exploring Different Loss Functions: Create a script to explore different loss functions like Mean Squared Error, Cross-Entropy, etc.

Simulating Different Optimization Algorithms: Write a script to simulate different optimization algorithms like Gradient Descent, Adam, etc.

Implementing a Simple Decision Tree: Create a script to implement and visualize a simple decision tree.

Visualizing High-Dimensional Data: Write a script to visualize high-dimensional data using techniques like PCA and t-SNE.

Implementing a Simple Support Vector Machine (SVM): Create a script to implement a simple SVM.

Exploring Different Kernel Functions: Write a script to explore different kernel functions in SVM.

Simulating Class Weighting: Create a script to simulate the effect of class weighting in a classification problem.

Implementing a Simple Naive Bayes Classifier: Write a script to implement a simple Naive Bayes classifier.

Exploring Different Scaling Techniques: Create a script to explore different scaling techniques like Min-Max Scaling, Standardization, etc.

Implementing a Simple Linear Regression Model: Write a script to implement a simple linear regression model.

Visualizing Residuals in Regression: Create a script to visualize residuals in a regression problem.

Simulating Multicollinearity: Write a script to simulate and detect multicollinearity in a dataset.

Implementing a Simple Logistic Regression Model: Create a script to implement a simple logistic regression model.

Exploring Different Evaluation Metrics: Write a script to explore different evaluation metrics like precision, recall, F1-score, etc.

Simulating Different Sampling Techniques: Create a script to simulate different sampling techniques like SMOTE.

Implementing a Simple K-Means Clustering Algorithm: Write a script to implement a simple K-Means clustering algorithm.

Visualizing Clusters Using Different Algorithms: Create a script to visualize clusters using different clustering algorithms.

Exploring Different Imputation Techniques: Write a script to explore different imputation techniques for missing data.

Simulating the Effect of Outliers: Create a script to simulate the effect of outliers in a dataset.

Implementing a Simple Neural Network from Scratch: Write a script to implement a simple feedforward neural network from scratch.

These ideas cover a wide range of concepts related to grokking, generalization, and other machine learning techniques. They can be used for educational purposes, research, or even as a starting point for more complex projects.