What are the different types of optimizers in Python for machine learning?
An example of an AI chatbot conversation about Python machine learning optimizers.
What are the different types of optimizers in Python for machine learning?
In Python, common optimizers include SGD, Adam, RMSprop, and Adagrad. Each has unique benefits.
Can you explain how Adam optimizer works?
Sure! Adam combines the advantages of two other extensions of stochastic gradient descent. It keeps track of both the average of the gradients and the squared gradients.
What are the advantages of using RMSprop?
RMSprop is great for non-stationary objectives and works well in practice for training deep networks.
Which optimizer is best for deep learning?
Adam is often recommended for deep learning due to its adaptive learning rate and efficiency.
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