2.7-ai-models-training-algos.markdown

← 2 AI (models)


v1 26.0609 (WIP… just a few note here… will later)

chats unformatted 26.0609 (see docx #607)

=1 a bigger W (weight) means a bigger loss rate. so during loss calculations the optimizer naturally chooses big W’s for modification. true? Not necessarily. A bigger weight does not automatically mean a bigger loss contribution or a bigger update. What matters is the gradient, not the weight itself. Recall: • optimizer.step() roughly does: • W = W - learning_rate * gradient The update size depends on: • gradient = d(loss)/dW not on: W ______________ Example: Large weight, tiny gradient W = 1000 gradient = 0.00001 Update: ΔW = very tiny Weight barely changes. ______________ Small weight, huge gradient W = 0.01 gradient = 50 Update: ΔW = large Weight changes dramatically. ______________ A useful intuition: Weight = current setting of a knob Gradient = how much the loss wants that knob turned The optimizer looks at the gradient: “Which knobs reduce loss the fastest?” and turns those. ______________ Now there is a related effect: For a neuron: z = Wx + b If: x = large or W = large then changing W can sometimes have a larger effect on the output, which can lead to larger gradients. But that is indirect. The optimizer is not saying: “This weight is big, so I’ll modify it.” It is saying: “This gradient is big, so I’ll modify it.” That distinction is important when you later study Adam. Adam’s entire job is essentially: • Don’t just look at the current gradient. Also remember previous gradients. Adjust step sizes intelligently. That’s why Adam is the next interesting topic after you’ve mastered: forward pass loss backward pass (gradients) optimizer.step() The gradient is the real signal. The optimizer follows the gradient, not the weight magnitude.

=2 In fact, I would argue that for your ZiptieAI “understand AI ASAP” approach, these four are the entire training loop: • forward pass

  1. loss
  2. backward pass
  3. optimizer.step() Everything else is details layered on top. ______________
  4. Forward pass pred = model(X) Question: Given the current weights, what output does the model produce? ______________
  5. Loss loss = loss_fn(pred, Y) Question: How wrong was the prediction? Output: single number such as: 0.86 0.42 0.03 ______________
  6. Backward pass loss.backward() Question: Which weights caused the error? Output: gradient for every weight Conceptually: weight_1 gradient = +0.7 weight_2 gradient = -0.2 weight_3 gradient = +0.01 … ______________
  7. Optimizer step optimizer.step() Question: How should the weights change? Simple SGD idea: new_weight = old_weight - learning_rate * gradient

Yahoo: To optimize model training efficiency, consider using the SGD loss algorithm with these steps: • Initialize model parameters randomly to start the training process. • Select a mini-batch of training data for each iteration. • Compute the loss function to evaluate model performance on the mini-batch. • Calculate the gradient of the loss with respect to the model parameters. • Update the model parameters using the gradients and a predefined learning rate. • Repeat the process for multiple epochs until convergence or desired accuracy is achieved.

Then repeat: forward loss backward step

forward loss backward step

forward loss backward step … until: loss ≈ small ______________ Once these four are completely intuitive, then Adam becomes much easier. Without understanding gradients, Adam looks like magic. With understanding gradients, Adam becomes: “Instead of blindly following today’s gradient, use a smarter estimate based on recent history.” That’s why I would study them in this order: A. forward pass B. loss C. gradients (backward) D. optimizer.step() E. SGD F. Adam You’ve already built enough demos (D1, D5, D6, D11) that you’re seeing these four lines over and over: pred = model(X) loss = loss_fn(pred, Y) loss.backward() optimizer.step() Those four lines are arguably the most important training code in all of deep learning.

 


26.0609 (v1 26.0609)