ML Optimization Methods and Techniques
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@workiniterations on Instagram: "If I had to start again, this is how I’d learn optimisation for ML: Objective Functions – what you’re actually minimizing – why loss does not equal accuracy – how loss shapes model behavior Resources: – StatQuest: Loss Functions – Deep Learning (intuition sections) – Steve Brunton (objective intuition) Gradients (the engine) – gradients as direction, not formulas – why steep does not mean its good – why zero gradient doesn’t mean “done” Resources: – 3Blue1Brown:
0:07
@workiniterations on Instagram: "If I had to start again, this is how I’d learn optimisation for ML: Objective Functions – what you’re actually minimizing – why loss does not equal accuracy – how loss shapes model behavior Resources: – StatQuest: Loss Functions – Deep Learning (intuition sections) – Steve Brunton (objective intuition) Gradients (the engine) – gradients as direction, not formulas – why steep does not mean its good – why zero gradient doesn’t mean “done” Resources: – 3Blue1Brown:
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