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https://github.com/jayvatti/mlnotebookarchives

My ML/DL Repository
https://github.com/jayvatti/mlnotebookarchives

deep-learning machine-learning neural-networks sklearn tensorflow

Last synced: 10 days ago
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My ML/DL Repository

Host: GitHub
URL: https://github.com/jayvatti/mlnotebookarchives
Owner: jayvatti
Created: 2023-06-02T18:30:20.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-07-01T04:25:24.000Z (5 months ago)
Last Synced: 2024-07-05T15:00:12.850Z (5 months ago)
Topics: deep-learning, machine-learning, neural-networks, sklearn, tensorflow
Language: Jupyter Notebook
Homepage: https://jayvatti.github.io/MLNotebookArchives/
Size: 11.9 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# [MLNotebookArchives](https://jayvatti.github.io/MLNotebookArchives/)

This repository contains a collection of Jupyter notebooks, each highlighting the application and understanding of a distinct machine learning model, from the basics to the more sophisticated. [`In Progress`]

---

## Content

- **[`Data folder`](/data/ReadMe.md):** A curated repository of datasets, serving as the foundation and fuel for the models crafted in these notebooks.

- **[`Kaggle folder`](/kaggle/ReadMe.md):**

## Notebook Structure

### **Basics**
1. **[`numpyBasics.ipynb`](numpyBasics.ipynb)**
2. **[`mnist.ipynb`](mnist.ipynb)**

### **Supervised Learning**

1. **[`singleFeatureLinearRegression.ipynb`](singleFeatureLinearRegression.ipynb)**
- **Description**:
- A beginner's journey into Linear Regression with a single input feature.
- Implementation with Sci-kit Learn library.
- Another version without any libraries.
- Plots of cost functions for learning rate selection.
- Bonus: A sketched line of best fit! (With and without Z-Score Normalization)
- **Dataset**: [height_weight.csv](/data/height_weight.csv)

2. **[`multipleFeatureLinearRegression.ipynb`](multipleFeatureLinearRegression.ipynb)**
- **Description**:
- Stepping up to multi-feature Linear Regression.
- Implementation using Sci-kit Learn.
- Another version without libraries.
- Includes cost function plots to guide the choice of learning rate.
- New: Added plots comparing the predictions from the SciKit Learn version and the version without libraries. Also included are plots comparing each method with the actual predictions.
- **Dataset**: [multipleFeatures.csv](/data/multipleFeatures.csv) - Because one feature is too mainstream!

3. **[`normalEquationLinearRegression.ipynb`](normalEquationLinearRegression.ipynb)**
- **Description**:
- Ditching iterations for equations, this notebook presents Linear Regression via the Normal equation.
- Employs the same dataset as the singleFeatureLinearRegression notebook
- Graphical representations may be missing, but the calculated weights and bias are similar to the ones from the gradient descent approach in [singleFeatureLinearRegression.ipnyb](singleFeatureLinearRegression.ipynb)
- **Dataset**: [height_weight.csv](/data/height_weight.csv)

4. **[`polynomialRegression.ipynb`](polynomialRegression.ipynb)**
- **Description**: Straight lines are too mainstream! This notebook explores Polynomial Regression using Sci-kit Learn.
- **Dataset**: [polyRegression.csv](/data/polyRegression.csv)

5. **[`binaryLogisticRegression.ipynb`](binaryLogisticRegression.ipynb)**
- **Description**:
- Flirting with classification, this notebook delves into Binary Logistic Regression using Logistic Loss and Sigmoid Functions.
- Includes a visualization of the sigmoid function and classification boundary, helping to understand how well the model separates classes. Classifying has never been so visually appealing!
- **Dataset**: [logisticRegression.csv](/data/logisticRegression.csv)

6. **[`regularizedLinearRegression.ipynb`](regularizedLinearRegression.ipynb)**
- **Description**:
- Regularization to the rescue! This notebook presents Regularized Linear Regression.
- Both library-based and bare-bones implementations are available.
- Cost function plots to select the best learning rate and lambda values.
- New: Added a plot to show the predictions of the custom model versus the original data, and another plot to compare the SciKit Learn version with the version without libraries.
- **Dataset**: [multipleFeatures.csv](/data/multipleFeatures.csv)

7. **[`regularizedLogisticRegression.ipynb`](regularizedLogisticRegression.ipynb)**
- **Description**:
- The sequel to regularized linear regression, featuring Regularized Logistic Regression.
- Implementations both with and without libraries.
- Cost function plots to guide the choice of learning rate and lambda values.
- **Dataset**: [logisticRegressionCircular.txt](/data/logisticRegressionCircular.txt)

### **Neural Networks**

1. **[`NeuralNetworkLinearRegression.ipynb`](NeuralNetworkLinearRegression.ipynb)**
- **Description**:
- From regressions to neurons, this notebook implements a single-layer neural network for Linear Regression.
- A regression line is sketched to highlight the model's predictions.
- **Dataset**: [linearRegressionSimple.csv](/data/linearRegressionSimple.csv)

2. **[`NeuralNetworkLogisticRegression.ipynb`](NeuralNetworkLogisticRegression.ipynb)**
- **Description**:
- Continuing the neural exploration, this notebook applies a single-layer neural network for Logistic Regression.
- A sketched sigmoid function after fine-tuning the weights and biases using Sci-kit learn.
- Notably, it focuses on tumor classification - the model might not cure cancer, but it sure can classify it!
- **Dataset**: [logisticRegressionTumorSingle.csv](/data/logisticRegressionTumorSingle.csv) - On a mission to segregate tumors!

3. **[`NeuralNetworkLayers_binaryClassification.ipynb`](NeuralNetworkLayers_binaryClassification.ipynb)**
- **Description**: Unleashing the power of deep learning, this notebook explores a multi-layer neural network for binary classification, featuring:
- Feature engineering using polynomial features up to degree 6.
- Visualizations of activation layers and 3D surface plots.
- Building a neural network from scratch, no high-level libraries involved.
- New: A 3D Decision Probability Plot, using Plotly - view or interact with it via [nbviewer](https://nbviewer.org/github/jayvatti/MLNotebookArchives/blob/main/NeuralNetworkLayers_binaryClassification.ipynb)
- **Dataset**: [logisticRegressionCircular.txt](/data/logisticRegressionCircular.txt)

4. **[`multiClass_softmax.ipynb`](multiClass_softmax.ipynb)**
- **Description**: Multiclass classification by employing softmax activation in neural networks. Highlights include:
- Comparing models built with and without 'from_logits = true', thus emphasizing the impact of this parameter on neural network performance.
- Featuring two different neural network architectures - a simple one with fewer units for a quick yet effective classification, and a moderately large one with more units to generate a more complex decision boundary.
- Vivid visualizations of decision boundaries via meshgrid and ravel.
- The data exploration journey begins with a custom-generated dataset using 'make_classification' from sklearn.datasets.
- **Dataset**: [multi_class_generated.csv](/data/multi_class_generated.csv) - Multi-class problems have never been more fun to solve!

### **Miscellaneous**

1. **[`dTrees.ipynb`](dTrees.ipynb)**
- **Description**:

2. **[`kmeans.ipynb`](kmeans.ipynb)**
- **Description**:

3. **[`anomalyDetection.ipynb`](anomalyDetection.ipynb)**
- **Description**:

4. **[`pca.ipynb`](pca.ipynb)**
- **Description**: