Principle:LaurentMazare Tch rs Backpropagation Training

Knowledge Sources	Learning Representations by Back-propagating Errors tch-rs
Domains	Deep_Learning, Optimization
Last Updated	2026-02-08 14:00 GMT

Overview

Training technique that combines loss computation, automatic gradient calculation via backpropagation, and parameter updates in a single optimization step.

Description

Backpropagation training implements the core training loop step: (1) zero all gradients from the previous iteration, (2) compute the loss from model predictions and targets, (3) backpropagate the loss to compute gradients for all trainable parameters via the chain rule, and (4) update parameters using the optimizer's update rule. This atomic step is repeated over mini-batches for multiple epochs until convergence.

Usage

Use this in every training loop iteration. The combined backward_step method ensures correct gradient zeroing, backpropagation, and parameter update ordering.

Theoretical Basis

Training Step:
  1. zero_grad()      -- Clear accumulated gradients
  2. loss.backward()  -- Compute dL/dw for all trainable w via chain rule
  3. optimizer.step() -- Update: w = w - lr * optimizer_update(dL/dw)

backward_step combines all three operations atomically.

Related Pages

Implemented By

• Implementation:LaurentMazare_Tch_rs_Optimizer_Backward_Step