$$Erring is human. To err is human. Seeing is believing. To see is to believe.$$

For the given above training data construct a neural architecture for language modelling for the following

case. Men$$on the dimension at each and every layer and for the weights between layers.

Case: "Using context window of size $4,$ design a neural network to predict the next word with candidate

vocabulary of size $10.$ Each vocabulary has a word embedding of dimension $3.$ Use two internal hidden

layers. First one with $6$ neurons and second with $4$ neurons. Each internal hidden layer's must be use sigmoid

ac$$va$$on func$$on$."$

If the above needs to be changed to accommodate the net to learn the word embedding as well, do the

necessary modi$$ca$$on in above case study requirement and pictorially draw the $$nal network $($No need to

draw all the connec$$on between immediate layers$).$