human
I am working on the file "Copy_of_Introduction_to_CNNs_Handout.ipynb".
The first task is:
Implementing Multilayer Perceptron
Next we are going to implement a mulilayer perceptron. Each layer weights the importance of the data from the previous layer, eventually terminating in the same sigmoid function to convert the representation vector of the input data into a probablity. Between each layer we now include a non-linearity or activation function, a function that allows the model to approximate non linear functions and filter data between layers.
Each layer of the MLP now looks like this $relu(w^Tx +b)$ and the full model becomes $\sigma(w^T relu(w^T relu(w^Tx + b)+ b) + b)$
Again we train this model with gradient decent
Defining the Model
Once again we are going to create a sub class of the pytorch class torch.nn.Module listing out the layers of our model in our __init__ method and the operations between those layers in the method called forward. The torch.nn.Module super class handles the implementation of the backpropogation.
Go a head and try to fill in what you think a Multilayer Perceptron Model would look like.
Here are some useful functions:

torch.nn.Linear -> Fully Connected Layer / Impliments $F(\boldsymbol x) = \boldsymbol w^T \boldsymbol x + \boldsymbol b$
torch.nn.Functional.relu -> Rectifying Linear Unit / Implements $max(0,x)$ 
Other activation functions can be found here: http://pytorch.org/docs/master/nn.html#non-linear-activation-functions 

torch.nn.Functional.softmax -> Softmax function / Implements $F(x) = \frac{e^{ \boldsymbol x}} {\sum_\limits{i \in dim \boldsymbol x} e^{\boldsymbol x_i} }$
torch.nn.Functional.log_softmax -> Log of the softmax function / Implements $F(x) = log(\frac{e^{ \boldsymbol x}} {\sum_\limits{i \in dim \boldsymbol x} e^{\boldsymbol x_i} })$
torch.Tensor.view -> allows you to reshape a Tensor (multi-dimentional vector)
Can you write Python program for it?

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Here you think a Multilayer Perceptron Model would look like?
assistant
#Here you think a Multilayer Perceptron Model would look like.

class MLP(nn.Module):
    def __init__(self):
        super(MLP, self).__init__()
        '''
        INSERT YOUR MODEL COMPONENTS HERE 
        '''
        return x
    def forward(self, x):
        '''
        LINK YOUR COMPONENTS HERE 
        '''
        return x