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Beginner Tutorial

Welcome to Neurogebra! This tutorial covers the fundamental concepts.

What is Neurogebra?

Neurogebra is a mathematics library designed for AI developers. It lets you work with mathematical expressions that are:

  • Symbolic - See and manipulate formulas
  • Numerical - Evaluate efficiently with NumPy
  • Trainable - Learn parameters from data
  • Educational - Understand what you're using

Step 1: Create a MathForge

MathForge is your main entry point:

from neurogebra import MathForge

forge = MathForge()

Step 2: Get an Expression

# Get ReLU activation
relu = forge.get("relu")

# See what it is
print(relu)           # Max(0, x)
print(relu.formula)   # LaTeX
print(relu.explain())  # Full explanation

Step 3: Evaluate

import numpy as np

# Single value
result = relu.eval(x=5)    # 5
result = relu.eval(x=-3)   # 0

# Array
result = relu.eval(x=np.array([-2, -1, 0, 1, 2]))
# [0, 0, 0, 1, 2]

Step 4: Compute Gradients

# Symbolic gradient
relu_grad = relu.gradient("x")
print(relu_grad)  # Derivative expression

# Evaluate gradient
grad_value = relu_grad.eval(x=2)

Step 5: Explore

# List all available expressions
all_exprs = forge.list_all()

# Category-wise
activations = forge.list_all(category="activation")
losses = forge.list_all(category="loss")

# Search
results = forge.search("smooth")

What's Next?

  • Try different activations: sigmoid, tanh, swish, gelu
  • Look at loss functions: mse, mae, huber
  • Move to the Intermediate Tutorial