by Suf | Nov 27, 2024 | Data Science, Python, Statistics
In this guide, we’ll dive into the process of calculating the p-value from a Z-score in Python, using clear and practical examples to make the concepts accessible. Whether you’re a student, a researcher, or just someone curious about statistics, understanding...
by Suf | Nov 25, 2024 | Data Science, Machine Learning, PyTorch
Introduction Autoencoders are neural networks designed to compress data into a lower-dimensional latent space and reconstruct it. They are useful for tasks like dimensionality reduction, anomaly detection, and generative modeling. In this tutorial, we implement a...
by Suf | Nov 23, 2024 | Data Science, Machine Learning, PyTorch
Table of Contents Introduction The MNIST Challenge Prerequisites Dataset Overview Exploratory Data Analysis (EDA) Building the Model Training the Model Model Evaluation Results Visualization Error Analysis Conclusion and Future Work Introduction Logistic regression is...
by Suf | Nov 21, 2024 | Data Science, Python, Statistics
How to Calculate a One-Proportion Z-Test in Python Table of Contents Introduction to One-Proportion Z-Tests Calculating the One-Proportion Z-Test in Python Calculating the Power Calculating the Effect Size Assumptions and Limitations Conclusion Try the One-Proportion...
by Suf | Nov 20, 2024 | Data Science, Programming, R, Tips
Table of Contents Introduction The Basics of set.seed() Why Use set.seed()? Best Practices Common Use Cases Troubleshooting Advanced Usage: Random Number Generators (RNGs) in R Conclusion Introduction In R programming, reproducibility is crucial for scientific...
by Suf | Nov 13, 2024 | Data Science, Machine Learning, PyTorch
PyTorch provides a variety of tensor operations, and understanding the differences between torch.mm, torch.matmul, and torch.mul is essential when working with tensor computations. Although they might look similar, these functions serve different purposes and operate...