I write about AI, Machine Learning, and Deep Learning in an easy-to-understand way.

— 12 Feb 2025

Local Minima in Deep Learning: Why Your ANN is Stuck (and How to Fix It!)

Local minima can trap your neural network, causing training to stall. In this post, we’ll break down what local minima are, how to detect them, and practical ways to escape using PyTorch, NumPy, and smart optimization techniques

— 11 Feb 2025

Finding Minima and Maxima Using Derivatives in Python (NumPy & PyTorch)

Learn how to find minima and maxima of functions using derivatives in Python with NumPy and PyTorch.

— 11 Feb 2025

T-Test: Spotting Real Differences, Not Just Coincidences

A T-test is a powerful statistical tool used to determine whether the difference between the means of two groups is statistically significant or just random noise.

— 10 Feb 2025

Reproducible Randomness in Deep Learning: Why Seeding Matters (With NumPy & PyTorch)

Ever trained a deep learning model twice and got different results? 🤔 That’s because randomness is everywhere in machine learning—weight initialization, data shuffling, mini-batch sampling, and more!

— 9 Feb 2025

Understanding Sampling & Sampling Variability: A Practical Guide with NumPy & PyTorch

Sampling plays a crucial role in deep learning, from selecting training data to mini-batch gradient descent. But did you know that sampling variability can impact your model’s performance?

— 9 Feb 2025

Variance Explained: Making Sense of Data Variability

Variance explained tells us how much of the data’s spread is accounted for by a model. It helps in PCA, regression, and machine learning to understand patterns better!

— 8 Feb 2025

Understanding Mean and Variance

Mean is the average value, showing the central trend, while variance measures how spread out the data is. Together, they help us understand patterns, consistency, and deviations in any dataset!

— 7 Feb 2025

Min, Max, Argmin & Argmax: Finding Extremes in Data

Min & Max give the smallest and largest values in a dataset, while Argmin & Argmax tell us their positions. Essential for optimization, data analysis, and machine learning!

— 6 Feb 2025

Entropy: Measuring Uncertainty in Data

Entropy quantifies randomness or uncertainty in a system. Used in information theory, ML, and decision trees, it helps us understand how unpredictable data is!

— 5 Feb 2025

The role of logarithms in deep-learning why and how they matter

Logarithm functions play a crucial role in deep learning, from stabilizing computations to improving model efficiency. In this post, I’ll break down why log functions are essential and how they’re applied in real-world deep learning scenarios.

$Logarithms: The Basics Explained Simply$

— 4 Feb 2025

Logarithms: The Basics Explained Simply

What is logarithms and how to use them in both NumPy and PyTorch

— 3 Feb 2025

Softmax Function: Turning Scores into Probabilities

The Softmax function is a key activation function in machine learning, particularly in classification tasks. It converts raw model outputs into probabilities, helping neural networks make decisions. In this post, we’ll explore how Softmax works, why it’s useful, and where it’s commonly applied.

$Matrix Multiplication$

— 2 Feb 2025

Matrix Multiplication

Matrix multiplication is a fundamental operation in linear algebra, widely used in computer graphics, physics, and machine learning. In this post, we’ll break down how matrix multiplication works, its rules, and why it’s essential for solving real-world problems.

$Understanding the Dot Product$

— 1 Feb 2025