https://github.com/kacemmathlouthi/deep-ml-problems
a collection of solved machine learning, linear algebra, and deep learning problems from deep-ml.com
https://github.com/kacemmathlouthi/deep-ml-problems
deep-learning linear-algebra machine-learning numpy python
Last synced: 7 months ago
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a collection of solved machine learning, linear algebra, and deep learning problems from deep-ml.com
- Host: GitHub
- URL: https://github.com/kacemmathlouthi/deep-ml-problems
- Owner: KacemMathlouthi
- License: mit
- Created: 2024-11-28T10:36:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-01T13:06:57.000Z (about 1 year ago)
- Last Synced: 2025-03-29T17:34:09.081Z (10 months ago)
- Topics: deep-learning, linear-algebra, machine-learning, numpy, python
- Language: Python
- Homepage: https://www.deep-ml.com/
- Size: 23.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deep ML Practice Problems Repository
Welcome to the **Deep ML Practice Problems** repository! This repository contains a collection of solved machine learning, linear algebra, and deep learning problems from [deep-ml.com](https://deep-ml.com). The solutions are implemented in Python and structured for learning, experimentation, and skill enhancement.
---
## Repository Structure
The repository is organized into directories, each representing a problem or concept:
```
.
├───2D Translation Matrix Implementation
├───Calculate 2x2 Matrix Inverse
├───Calculate Accuracy Score
├───Calculate Correlation Matrix
├───Calculate Image Brightness
├───Calculate Jaccard Index for Binary Classification
├───Calculate Mean by Row or Column
├───Calculate R-squared for Regression Analysis
├───Calculate Root Mean Square Error (RMSE)
├───Feature Scaling Implementation
├───Implement F-Score Calculation for Binary Classification
├───Implement Precision Metric
├───Implement Recall Metric in Binary Classification
├───Implement ReLU Activation Function
├───Implementation of Log Softmax Function
├───Leaky ReLU Activation Function
├───Matrix Times Vector
├───One-Hot Encoding of Nominal Values
├───Scalar Multiplication of a Matrix
├───Sigmoid Activation Function Understanding
├───Single Neuron
├───Softmax Activation Function Implementation
├───Transpose of a Matrix
```
---
## Features and Topics Covered
This repository covers a variety of fundamental concepts and operations in machine learning and deep learning:
1. **Linear Algebra**
- Matrix operations (e.g., transpose, scalar multiplication, matrix-vector multiplication).
- Inverse and determinant calculations.
- One-hot encoding and feature scaling.
2. **Performance Metrics**
- Accuracy, precision, recall, F-score.
- R-squared and RMSE for regression.
- Jaccard index for binary classification.
3. **Activation Functions**
- Sigmoid, ReLU, Leaky ReLU, and Softmax.
- Log-Softmax implementation.
4. **Miscellaneous**
- Image brightness calculation.
- Correlation matrix computation.
- Understanding the behavior of a single neuron.
---
## License
This repository is open-sourced under the [MIT License](LICENSE).