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Module 4: Backpropagation & Optimizers (The Feedback Loop)

📚 Module 4: Backpropagation & Optimizers

Course ID: DL-404
Subject: The Feedback Loop

A Neural Network starts out knowing nothing. It learns by making mistakes and being corrected.

🏗️ Step 1: Backpropagation (The “Feedback”)

Once we have an Error (Loss), we tell every single neuron how to change its weights to be better next time.

🧩 The Analogy: The Chain of Command

Imagine a company makes a mistake. The CEO (Output) tells the Managers (Hidden), who tell the Workers (Input): “Fix your process!”

🏗️ Step 2: Optimizers (The “Strategy”)

  1. Gradient Descent: Taking small steps downhill.
  2. Adam: The “Smart Car” optimizer. It speeds up on flat roads and slows down for sharp turns.

🥅 Module 4 Review

  1. Loss Function: Measures how wrong the model is.
  2. Backpropagation: Passes error signals backwards.
  3. Optimizer: The strategy for updating weights (e.g., Adam).
  4. Epoch: One full pass through the dataset.

:::tip Slow Learner Note Learning is just Trial and Error. The “Deep” in Deep Learning just means the error signal has a long way to travel back to the start! ::: Riverside. Riverside.