Interview1

# PyTorch Basics and Neural Network Training

# Exercise 1: Tensor Operations

# Problem:
# a) Create a PyTorch tensor 'x' of shape (3, 4) with random values between 0 and 1.
# b) Create a tensor 'y' of shape (4, 2) with all elements initialized to 5.
# c) Multiply tensor 'x' with tensor 'y' and store the result in tensor 'z'.
# d) Calculate the mean value of tensor 'z' along the columns (axis=0).

# Exercise 2: Neural Network Training

# Problem:
# a) Create a simple feed-forward neural network with 2 input units, 3 hidden units, and 1 output unit.
# b) Define the appropriate activation function for the hidden layer.
# c) Generate random input data 'inputs' of shape (5, 2) and random target data 'targets' of shape (5, 1).
# d) Implement a training loop to train the neural network for 100 epochs using the Mean Squared Error (MSE) loss.

# Exercise 3:
# In gradient descent, what is the role of the learning rate parameter? How does it impact the convergence of the optimization process?

# Exercise 4:
# In stochastic gradient descent (SGD), what is the key difference compared to traditional gradient descent? 
  How does the use of mini-batches affect the optimization process in terms of efficiency and convergence?