Building Deep Learning models without callbacks is like driving a car with no functioning brakes -- you have little to no control over the whole process that is very likely to result in a disaster. In this article, you will learn how to monitor and improve your Deep Learning models using Keras callbacks like ModelCheckpoint and EarlyStopping. A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training. You define and use a callback when you want to automate some tasks after every training/epoch that help you have controls over the training process.
The next proven way to cater to leads efficiently is when you assist them via your website or landing page. There are many B2B users who take the support of live chats or callback software where they immediately cater to their client's needs. For instance, with an efficient callback software available in the market, you can use a chatbot to immediately assist your leads on arrival and help them reach out to you quickly or schedule a convenient time according to their preference. This is great because even when you are on a break or are away, you still receive alerts of a new lead showing interest.
One of the harder problems that chatbot developers face is, how to maintain the context of conversation. While all the popular frameworks provide an opinionated take on how to maintain this context, none of them seem to be either simple or complete. Here let me introduce a reactive approach to maintain the context. The elegance of this approach is that the pushed callback captures the state of variables as a part of its closure context. When the callback is called, this state is retained and we can time travel back to the state when the context was actually set.