When training a neural network, the choice of optimizer can have a significant impact on the training dynamics and the final performance of the model. SGD (Stochastic Gradient Descent) and Adam are two popular optimizers, and they have different characteristics: Basic Differences: SGD: This is the classical version of gradient descent optimization where the model updates its parameters in the direction of the negative gradient. Adam (Adaptive Moment Estimation): Combines the ideas of Momentum (moving average of gradients) and RMSprop (moving average of squared gradients) to adjust the learning rate for each parameter individually. Learning Rate: SGD: Typically uses a constant learning rate, although there are variants with adaptive learning rates. Adam: Computes adaptive learning rates for different parameters from estimates of the first and second moments of the gradients. This often leads to faster convergence. Noise: SGD: Updates can be noisy (especially in the case of pure SGD without any momentum), which can be beneficial because this noise can help escape shallow local minima. However, it may also lead to slower convergence. Adam: Due to its adaptive nature, it tends to be more stable than pure SGD. However, this can sometimes lead to premature convergence or getting stuck in sharp minima, which might not generalize well. Validation Accuracy Dynamics: SGD: Can lead to smoother curves in terms of validation accuracy because of its consistent update rule. Adam: Given its adaptive nature, sometimes the updates can be aggressive, leading to oscillations or choppier curves in terms of validation accuracy. Generalization: There's ongoing research in deep learning that sometimes suggests models trained with SGD generalize better than those trained with adaptive methods like Adam, especially when trained with proper regularization and learning rate schedules. The noise introduced by SGD can act as a form of implicit regularization. Convergence Speed: In many cases, especially in the early stages of training, Adam can converge much faster than SGD because of its adaptive properties. However, SGD, with a well-tuned learning rate (or learning rate schedule), might lead to better generalization in the long run. In Summary: The choppier validation accuracy curve observed with Adam compared to SGD could be attributed to Adam's adaptive learning rate adjustments, which can sometimes cause oscillations in performance. However, the choice between Adam and SGD should be based on the specific problem, dataset, and the goals of training. Sometimes, a combination of the two (e.g., starting training with Adam and then switching to SGD) can be effective. Always validate with your own experiments!