The assignment questions is in the Machine Learning in Finance Assignments pdf files. There are 11...

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The assignment questions is in the Machine Learning in Finance Assignments pdf files. There are 11 questions which are only theory type questions related to Machine learning and python applying it into finance. The expert should know deep learning and neural networks and model evaluation and know its application in Finance. Please these are easy questions and I have provided all related documents to be able to answer the questions. There is a mini case study in the attached support document in the pdf deep learning and neural networks file which the expert has to consult and answer some questions. Please make it a very good price for me as I have provided all necessary material to answer the 11 theoritical questions and they are straight forward. Please make it very professional and high level.

Valupadasu · Accepted Answer

1. In the given neural network-
a. Number of input variables – 3
b. Number of output variables – 2
c. Number of neurons in the neural network – 17
d. Depth of neural network – 5
e. Graph for Input layers, hidden layers and output layers –
2. List of activation functions commonly used in an artificial Neural Network –
We can use sigmoid , TanH or Hyperbolic Tangent, Rectified Linear Unit and Softmax as  activation functions
3. a.  Forward propagation in a neural network – 
· Forward propagation means the calculation and storage of intermediate variables along with their outputs from the neural networks in an order starting from input layer to output layer.
· The input will be given in the forward direction through a network and each hidden layer in the network accepts the input data, process the same as per the activation function and it will be forwarded further to its successive layer.
b. Back ward propagation in a neural network – 
· Back propagation algorithm searches for the minimum value in the error function in its weight space with the help of either delta rule or gradient descent. Then the weights that minimizes the error will be considered as the right fit for our learning problem.
· The main idea behind back propagation is to minimize the cost function upon adjusting network’s weights and biases. As we discussed earlier the level of adjustment will be decided by the gradients of the cost function in relation to its parameters. 
4.  Recurrent Neural Network – 
Recurrent Neural Network (RNN) are a type of Neural Network in which the output from the earlier step will be given as input to the current step. they have a “memory” which can remember all information about what has been calculated so far. It uses the same parameters for each input because it performs the same task on all the inputs or hidden layers in order to produce the output. which reduces the complexity of parameters, which separates it from other neural networks.

The assignment questions is in the Machine Learning in Finance Assignments pdf files. There are 11 questions which are only theory type questions related to Machine learning and python applying it...

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