In today’s post, I’m excited to share a beginner-friendly guide on how to train a neural network using Keras on the famous MNIST dataset. Whether you’re new to deep learning or just looking for a c...
Feature Scaling in Machine Learning: A Practical Guide When training machine learning models, having features on vastly different scales can slow down learning or even lead to suboptimal performanc...
[J(w,b) = \frac{1}{2m} \sum_{i=1}^m \left(f_{w,b}\bigl(x^{(i)}\bigr) – y^{(i)}\right)^2] The cost function in linear regression is a way to measure how well a model’s predictions match the actual ...
Matplotlib is a cornerstone in the data science toolkit. Whether you’re prototyping in Jupyter or preparing publication‐quality figures, its flexibility makes it indispensable. In this post, we’ll ...
Pandas is a powerful Python library that has become a cornerstone in data science. It offers fast, flexible, and expressive data structures designed to work with structured (tabular, multidimension...
In the world of data science and machine learning, efficiency is key. NumPy is one of the most powerful libraries in Python, providing high-performance multidimensional arrays and a collection of r...
Machine learning covers a broad range of techniques to solve problems—from predicting continuous values to classifying images. In this post, I’ll break down the core ideas behind some of the most i...
At //buil/ the Microsoft Developer Conference in San Francisco, CA presenting my enanched controls supporting ink and voice. Source code available on GitHub
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