user: I am on a week long vacation to India and specifically want to explore Himalayan state of Himachal Pradesh. How should I plan my trip ?

assistant: Firstly , fly to Delhi or Chandigarh. These cities are well connected via flights from across the world.  Next , take an overnight bus to Manali.  There are ample buses available connecting Manali from any cities.  Spend 4 days of your time  enjoying local cuisine and sight seeing.   Do carry wollens  as temperature drops sub zero during the night. Next , head towards Shimla and spend 2 days here for local sightseeing enjoying marvels of British architectures and old mall road.  Wrap up your trip travelling back to Delhi or Chandigarh. Spend time doing some local shopping before you fly back home.

user: Why did the cow jump over the moon?

assistant: The cow wanted to jump to the moon because it had run out of milk for its calf. The calf was crying day and night because of how hungry it was. The mother cow looked up at the night sky, saw the milky-colored moon in the sky, and thought if it jumped to the moon, its udders would be full again. It practiced over and over, jumping as high as it could. Once it was finally ready that it jumped over the moon. The courage that it gained filled the udders with milk once again.

user: given the following text, tell me the most popular method for training multi-layer perceptrons today and explain how it works

assistant: <think>
Today, the most popular method for training multi-layer perceptrons (MLPs) is back-propagation. The terminology "back-propagating errors" was introduced in 1962 by Frank Rosenblatt, but he did not know how to implement this, although Henry J. Kelley had a continuous precursor of backpropagation already in 1960 in the context of control theory. Modern backpropagation is actually Seppo Linnainmaa's general reverse mode of automatic differentiation (1970) for discrete connected networks of nested differentiable functions. It is an efficient application of the chain rule (derived by Gottfried Wilhelm Leibniz in 1673) to networks of differentiable nodes. In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard. In 1985, David E. Rumelhart et al. published an experimental analysis of the technique. Many improvements have been implemented in subsequent decades.

During backpropagation, the output values are compared with the correct answer to compute the value of some predefined error-function. The error is then fed back through the network. Using this information, the algorithm adjusts the weights of each connection in order to reduce the value of the error function by some small amount. After repeating this process for a sufficiently large number of training cycles, the network will usually converge to some state where the error of the calculations is small. In this case, one would say that the network has learned a certain target function. To adjust weights properly, one applies a general method for non-linear optimization that is called gradient descent, due to Augustin-Louis Cauchy, who first suggested it in 1847. For this, the network calculates the derivative of the error function with respect to the network weights, and changes the weights such that the error decreases (thus going downhill on the surface of the error function). For this reason, back-propagation can only be applied on networks with differentiable activation functions.</think>
Today, the most popular method for training multi-layer perceptrons (MLPs) is back-propagation. During backpropagation, the output values are compared with the correct answer to compute the value of some predefined error-function. The error is then fed back through the network. Using this information, the algorithm adjusts the weights of each connection in order to reduce the value of the error function by a small amount. After repeating this process for a sufficiently large number of training cycles, the network will usually converge to a state where the error of the calculations is small. In this case, one would say that the network has learned a certain target function