¡Hola, Mundo!

rand It returns a tensor filled with Random floats from a uniform distribution on the interval [0, 1]. Uniform distribution on [0, 1] means : Every number between 0 and 1 has an equal probability of being selected. It doesn't mean the numbers will be evenly spaced or look "uniform" when you look at a few samples. For example, torch.rand(4)=> tensor([0.7529, 0.8579, 0.6870, 0.0051]) Each of these..

sample_size = 10000 rand=np.random.rand(sample_size) randn=np.random.randn(sample_size) randint=np.random.randint(low=10, high=15, size=10) random_integers=np.random.random_integers(1, 6, 10) pdf, bins, patches = plt.hist(rand, bins=20, range=(0, 1), density=True) plt.title('rand') plt.show() pdf, bins, patches = plt.hist(randn, bins=20, range=(-4, 4), density=True) plt.title('randn') plt.show()..

randint randint(start, end, (tensor size))torch.randint(3, 5, (3,))>>>tensor([4, 3, 4])torch.randint(3, 10, (2, 2))>>>tensor([[4, 5], [6, 7]])scatter VS scatter_ - scatter_(dim, index, value)- scatter(dim, index, value) dim - The dimension along which to scatter0 = scatter along rows (vertical)1 = scatter along columns (horizontal)index - A tensor containing the indices where to place the..