Pytorch fundamentals

Introduction to tensors	Tensors are the basic building block of all of machine learning and deep learning.
Creating tensors	Tensors can represent almost any kind of data (images, words, tables of numbers).
Getting information from tensors	If you can put information into a tensor, you'll want to get it out too.
Manipulating tensors	Machine learning algorithms (like neural networks) involve manipulating tensors in many different ways such as adding, multiplying, combining.
Dealing with tensor shapes	One of the most common issues in machine learning is dealing with shape mismatches (trying to mixed wrong shaped tensors with other tensors).
Indexing on tensors	If you've indexed on a Python list or NumPy array, it's very similar with tensors, except they can have far more dimensions.
Mixing PyTorch tensors and NumPy	PyTorch plays with tensors (torch.Tensor), NumPy likes arrays (np.ndarray) sometimes you'll want to mix and match these.
Reproducibility	Machine learning is very experimental and since it uses a lot of randomness to work, sometimes you'll want that randomness to not be so random.
Running tensors on GPU	GPUs (Graphics Processing Units) make your code faster, PyTorch makes it easy to run your code on GPUs.

Introduction to tensors

• Fundamental building block of ML, job is to represent data in numerical way.

  • Creating tensors

    • can use torch.tensor()

    • Scalar

      • a single number and in tensor-speak it is a zero dimension tensor.

      • Although scalar is a single number it is of type torch.tensor

      scalar=torch.tensor(7)  # initialize a scalar tensor as 7
      scalar.ndim  # gives the number of dimension of scalar    

    • Vector

      • Single dimension tensor that can contain many numbers

      vector = torch.tensor([7, 7])   #creates the vector tensor 
      vector.ndim #gets the number of dimension of vector

    • Matrix

      • Matrices are flexible as vectors except they got an extra dimension

      matrix= torch.tensor([[7,8],[9,10]]) # initialize the matrix matrix.ndim # get the number of dimension of matrix matrix.shape # shape gives us number of rows and columns

    • Random tensors

    #Create a random tensor of size (3, 4)

    random_tensor = torch.rand(size=(3, 4)) random_tensor, random_tensor.dtype

  • # Create a random tensor of size (224, 224, 3)
    random_image_size_tensor = torch.rand(size=(224, 224, 3))
    random_image_size_tensor.shape, random_image_size_tensor.ndim

• Zeros and ones