π€ AI, Explained Simply
Clear intuition. Minimal math. Real understanding.
We at this simple blog take a different path in explaining dense equations, and AI code algorithms. We start with intuition, then explain why algorithms work, show the math, then turn everything into a simple code.
The goal is not to hide the math β but to make it understandable.
How This Site Works
Think of this site as an interactive learning notebook:
π Concepts are broken down step by step.
π§ͺ Code examples are small, runnable, and explainable.
π Visuals show the intuition under the hood .
π Real-world analogies and examples.
Explore the Topics
π§ Fundamentals
Understand the building blocks of modern AI:
gradient descent and optimization
loss functions and error signals
momentum, RMSProp, Adam
activation functions and learning dynamics
π Start here if you want to truly understand how machines learn.
𧩠Neural Network Architectures
Learn how neural networks are designed:
- Convolutional Neural Networks (CNNs) for images
- Recurrent Neural Networks (RNNs) for sequences and time series
- How architectural choices affect learning and performance
π§ This section focuses on structure, inductive bias, and why certain designs work better than others.
Who This Is For
- π Students learning AI for the first time
- π©βπ» Engineers who want stronger intuition
- π€ Anyone curious about how learning algorithms actually work
The Philosophy
If you canβt explain an algorithm simply, you donβt understand it well enough.
Comments