In the last chapter, we learned how MAML is used for finding an optimal parameter that's generalizable across several tasks. We saw how MAML computes this optimal parameter by calculating meta gradients and performing meta optimization. We also saw adversarial meta learning, which acts as an enhancement to MAML by adding adversarial samples and allowing MAML to wrestle between clean and adversarial samples to find the optimal parameter. We also saw CAML—or, context adaptation for meta learning. In this chapter, we'll learn about Meta-SGD, another meta learning algorithm that's used for performing learning quickly. Unlike MAML, Meta-SGD will not just find the optimal parameter, it will also find the optimal learning rate and an update direction. We'll see how to use Meta-SGD in supervised and reinforcement learning settings. We'll also see how to build Meta-SGD from scratch. Going ahead, we'll learn about the Reptile algorithm, which acts an improvement to MAML. We'll see how Reptile differs from MAML and then we'll cover how to use Reptile in sine wave regression tasks.

In this chapter, you'll learn about the following:

• Meta-SGD
• Meta-SGD in supervised learning
• Meta-SGD in reinforcement learning
• Building Meta-SGD from scratch
• Reptile
• Sine wave regression using Reptile