Deep Learning with PyTorch

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It sounds like a mouthful, but trust me, it’s not as complicated as it seems.

First, let’s break down what we mean by “deep learning” and “PyTorch”. Deep learning is essentially using artificial neural networks to teach computers how to do stuff on their own (like recognizing images or understanding speech). PyTorch is just a fancy tool that helps us build these neural networks.

So, let’s say you have this massive dataset of cat pictures and dog pictures. You want your computer to be able to look at a new picture and figure out whether it’s a cat or a dog (without any help from you). That’s where deep learning comes in! We can use PyTorch to build a neural network that will learn how to do this on its own by looking at all those cat and dog pictures.

Here’s an example of what the code might look like:

# Import necessary libraries
import torch
from torch import nn, optim
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor

# Load data
train_data = CIFAR10('path/to/dataset', train=True, transform=ToTensor())
test_data = CIFAR10('path/to/dataset', train=False, transform=ToTensor())

# Define model architecture (in this case, a simple convolutional neural network)
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=5) # input: 3 channels (RGB), output: 64 filters of size 5x5
        self.pool1 = nn.MaxPool2d(kernel_size=2) # pooling layer to reduce the spatial dimensions by half
        ...
        
    def forward(self, x):
        x = self.conv1(x) # apply convolution operation with 64 filters of size 5x5
        x = self.pool1(x) # apply max pooling operation to reduce the spatial dimensions by half
        ...
        
# Define loss function (in this case, cross-entropy loss) and optimizer (in this case, SGD with momentum)
criterion = nn.CrossEntropyLoss() # calculates the loss between predicted and actual labels
optimizer = torch.optim.SGD(my_model.parameters(), lr=0.01, momentum=0.9) # updates the model's parameters based on the calculated loss

# Train model on training data for a certain number of epochs (iterations through the dataset)
for epoch in range(num_epochs):
    running_loss = 0.0 # initialize loss variable to keep track of total loss over all batches
    for i, data in enumerate(train_loader):
        inputs, labels = data[0], data[1] # get input and label tensors from the batch
        
        # zero out gradients before running backpropagation (to prevent accumulation)
        optimizer.zero_grad() # sets the gradients of all model parameters to zero
        
        # forward pass: run model on input and calculate output predictions
        outputs = my_model(inputs) # feeds the input data into the model and generates output predictions
        
        # backward pass: calculate loss based on predicted vs actual labels, then update weights using backpropagation
        loss = criterion(outputs, labels) # calculates the loss between predicted and actual labels
        loss.backward() # backpropagates the loss through the model to update the gradients of the model parameters
        optimizer.step() # updates the model's parameters based on the calculated gradients
        
        # print current batch loss and accuracy (optional)
        running_loss += loss.item() * inputs.size(0) # calculate total loss for this batch by multiplying the loss value with the number of examples in the batch
        ...

So, essentially what we’re doing here is loading our dataset, defining a neural network architecture (in this case, a simple convolutional neural network), and then training it on that data using PyTorch. The code above shows an example of how to do all these things in Python!

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