AI-ML-VERSE / STUDY-NOTEBOOK / REV-1.0
INTERACTIVE AI LABORATORY

Understand
AI.
Visually.

Interactive explanations of CNNs, backpropagation, normalization, and modern ML architectures — built for developers who want deep understanding, not just theory.

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DWG NO: AIMLV-001 | SCALE: 1:1
LEARNING SEQUENCE / REV 1.0

ML Study Roadmap

Each module builds on the last — follow from Step 1 to master machine learning progressively.

STEP1
Foundation

Activation Functions

Neural non-linearity — how networks learn curves, not just lines.

STEP2
Optimization

Gradient Descent

The core optimizer. Watch the ball roll down the loss surface.

STEP3
Training

Backpropagation

Chain rule in motion — gradients flowing backwards through graphs.

STEP4
Architecture

Neural Network Playground

Build networks live, see shape propagation and forward pass.

STEP5
Techniques

3D Normalization

Batch, Layer, and RMSNorm shown geometrically in 3D tensor space.

STEP6
Vision

Convolution Visualizer

Filters sliding over images — strides, padding, and feature maps.

STEP7
Classification

Decision Boundary

How classifiers carve up feature space geometrically.

STEP8
Generalization

Overfitting Demo

Tune complexity on noisy data — experience the bias-variance tradeoff.

✓ CURRICULUM COMPLETE
ALL MODULES / ML VISUAL PLAYGROUND

Interactive Visualizers

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Architecture Playground

Scale architectures with memory and compute.

Normalization Flow

Batch, Layer, RMSNorm transformations.

Backpropagation

Chain rule through a computation graph.

Activation Functions

ReLU, Sigmoid, GELU visualized.

Convolution Visualizer

Filters and pooling animated live.

Loss Function Explorer

3D loss surfaces with gradient descent.

DEVELOPER NOTE

B.Tech final year student — built to track and visualize ML concepts learned over 4 years. Found an error? Please let me know.

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