Verified65% confidenceDecision RuleExact time
Using the Adam optimizer with a default learning rate of 1e-3 as a starting point is a robust entry strategy for deep learning.
3
Sources
65%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Training Machine Learning Models from Scratch: A Complete Beginner's Guide
redditr/learnmachinelearning7/11/2026
Related Claims
VerifiedThe Adam optimizer was proposed in 2014, combining momentum and adaptive learning rates, and has become the most widely used optimizer today.73% similarVerifiedAdaptive learning rate optimizers such as Adam and RMSProp further improve convergence speed and stability over SGD.72% similarUnverifiedThe training configuration uses a batch size of 4, the Adam optimizer, and a learning rate of 2e-4.68% similarUnverifiedo3-mini-high has been trained with reinforcement learning for reasoning, achieving reasoning capabilities close to o1 at a smaller scale.67% similarUnverifiedThe Cosine Annealing learning rate scheduling strategy is particularly suitable for LoRA training, where a high learning rate in the early stages of training rapidly captures features and then shrinks in the later stages to prevent overfitting.67% similar
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