Optimizers
In machine learning, optimizers are algorithms or methods used to change the attributes of the neural network, such as weights and learning rate, to reduce the losses. Optimizers help in improving the accuracy and performance of the neural network.
Types and Functions of Optimizers
Common optimizers include Stochastic Gradient Descent (SGD), Adam, RMSprop, and Adagrad. Each optimizer has its approach to navigating the loss landscape of a neural network to find the optimal set of parameters, balancing the speed and stability of the learning process.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
untouchable mode.
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Several people are typing about AI/ML security. 
Come join us and 1000+ others in a chat that’s thoroughly SFW.
