First, joint architecture search. This involves using a technique called reinforcement learning to automatically generate new architectures for our neural network models. Instead of manually designing each layer and connection between them (which can be time-consuming and tedious), we allow the computer to figure it out on its own by providing it with some basic guidelines and letting it experiment until it finds a solution that works best for our specific problem.

For example, let’s say we want to create a neural network model that can accurately predict whether or not someone is likely to buy a product based on their browsing history. We might provide the computer with some initial parameters (such as the number of layers and the type of activation functions) and then allow it to generate new architectures by tweaking these settings over time.

Now, recipe search. This involves using another technique called transfer learning to take an existing neural network model that has already been trained on a similar task (such as image classification or language translation), and then adapting it for our specific problem. Instead of starting from scratch each time we want to create a new model, we can use this pre-trained “recipe” as a starting point and then fine-tune it using our own data.

For example, let’s say we have a neural network model that has already been trained on the task of image classification (such as recognizing cats vs dogs). We might take this existing recipe and then modify it slightly to better suit our needs by adding or removing certain layers, changing the activation functions, etc. This can help us save time and resources since we don’t have to start from scratch each time we want to create a new model for a similar task.

So, how do joint architecture search and recipe search work together in FbNetv3? Well, by combining these two techniques, Facebook has been able to significantly improve the performance of their neural network models while also reducing the amount of time and resources required to train them. By allowing the computer to automatically generate new architectures using reinforcement learning, they can find solutions that are tailored specifically for our problem (such as predicting whether or not someone is likely to buy a product based on their browsing history). And by using transfer learning to take an existing recipe and then fine-tune it using our own data, we can save time and resources since we don’t have to start from scratch each time we want to create a new model for a similar task.

It might sound like a bunch of fancy jargon at first, but once you break it down into simpler terms (like joint architecture search and recipe search), it becomes much easier to understand how this technology works and why it’s so exciting for the future of AI.