Torch online coding platform

Torch is a machine learning library for Python that provides a wide range of algorithms and tools for deep learning, computer vision, and natural language processing. It is built on the Lua programming language and is designed to be fast and flexible, making it a popular choice for research and development in the field of machine learning.

One of the key features of Torch is its support for GPU acceleration, which allows it to make use of the parallel processing power of graphics processing units (GPUs) to speed up the training of deep learning models. This makes it possible to train complex models with large datasets in a relatively short amount of time.

Torch also includes a number of popular deep learning libraries, such as TorchVision, which provides tools for computer vision tasks, and TorchText, which provides tools for natural language processing tasks.

Here is a simple example of how to use Torch to define and train a simple neural network:

Copy codeimport torch

# Define the model
model = torch.nn.Sequential(
torch.nn.Linear(10, 20),
torch.nn.ReLU(),
torch.nn.Linear(20, 1)
)

# Define a loss function and an optimizer
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

# Generate some fake data
x = torch.randn(64, 10)
y = torch.randn(64, 1)

# Training loop
for t in range(500):
# Forward pass: compute predicted y by passing x to the model
y_pred = model(x)

# Compute and print loss
loss = loss_fn(y_pred, y)
print(t, loss.item())

# Zero gradients, perform a backward pass, and update the weights.
optimizer.zero_grad()
loss.backward()
optimizer.step()

In this example, we define a simple neural network with two fully-connected layers and a ReLU activation function. We then define a loss function (mean squared error) and an optimizer (stochastic gradient descent) and use them to train the model on some fake data. The training loop iteratively performs a forward pass to make predictions, computes the loss, performs a backward pass to compute the gradients, and updates the weights of the model using the optimizer.