Neural Networks + Visualization Tutorial

cds.ml is a from-scratch neural network library (MLP, dense layers, Adam optimizer) — no PyTorch, no NumPy. Pair it with cds.data_analysis for terminal-native ASCII charts to inspect results without a plotting backend.

1. Build and train an MLP

Define the network as a list of Layers with explicit input/output sizes and activations.

from cds.ml import MLP, Layer

# XOR-like logic: input [x1, x2] -> output [x1 OR x2]
X = [[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]]
y = [[0.0], [1.0], [1.0], [1.0]]

net = MLP([
    Layer(2, 4, activation="relu"),
    Layer(4, 1, activation="sigmoid"),
])

Before training the outputs sit near 0.5 (random init):

Initial predictions (Untrained):
  In: [0.0, 0.0] -> Out: 0.5000
  In: [0.0, 1.0] -> Out: 0.3825
  ...

Train with the built-in Adam optimizer (momentum state persisted across steps):

history = net.train(X, y, epochs=200, lr=0.1)
print(f"Final loss: {history['final_loss']}")

After 200 epochs the network has learned the OR function:

Final predictions (Trained):
  In: [0.0, 0.0] -> Out: 0.0406
  In: [0.0, 1.0] -> Out: 0.9908
  In: [1.0, 0.0] -> Out: 0.9915
  In: [1.0, 1.0] -> Out: 1.0000

history carries final_loss, iterations, and a converged flag so you can decide programmatically whether to keep training.

2. Visualize results in the terminal

cds.data_analysis ships plot_bar and plot_line — scale-aware ASCII charts that need no matplotlib.

from cds.data_analysis import plot_bar, plot_line
import math

stats = {"Quantum": 95.5, "Signals": 88.2, "Math": 92.0, "ML": 99.1}
print(plot_bar(stats, title="Module Readiness Score"))

wave = [math.sin(x * 0.2) for x in range(50)]
print(plot_line(wave, title="Generated Sine Wave (ASCII)"))

Module Readiness Score
──────────────────────
Quantum  | ████████████████████████████████████████████████ (+95.50)
Signals  | ████████████████████████████████████████████ (+88.20)
...

The line plot auto-scales to [min, max] and renders with • markers, so you can eyeball loss curves or signal shapes straight from a terminal.

3. The pure-Python advantage

Because every layer, optimizer step, and chart is readable Python, this is an ideal setup for teaching how backpropagation and Adam actually work — you can set breakpoints inside the training loop and watch gradients flow.

Run the full demo:

python examples/ml_and_viz_demo.py