Deep Learning PyTorch Course, Deep Learning Learning Process

Deep learning is a branch of artificial intelligence and a collection of machine learning methods based on artificial neural networks. One of the core technologies of deep learning widely used in various fields today is PyTorch. PyTorch is popular among many researchers and developers for its easy-to-use dynamic computation graph and powerful tensor operation capabilities. In this article, we will take a detailed look at the learning process of deep learning using PyTorch.

1. Basics of Deep Learning

Deep learning is a method of analyzing and predicting data through artificial neural networks. An artificial neural network is a model that mimics the structure and function of biological neural networks, where each node represents a nerve cell and is connected to transmit information.

1.1 Structure of Artificial Neural Networks

Artificial neural networks mainly consist of an input layer, hidden layers, and an output layer:

  • Input Layer: The layer where data enters the neural network.
  • Hidden Layer: A layer that performs intermediate calculations, which can have one or more instances.
  • Output Layer: The layer that generates the final result of the neural network.

1.2 Activation Function

The activation function determines whether each neuron in the neural network will be activated. Commonly used activation functions include:

  • Sigmoid: $f(x) = \frac{1}{1 + e^{-x}}$
  • ReLU: $f(x) = max(0, x)$
  • Tanh: $f(x) = \tanh(x)$

2. Introduction to PyTorch

PyTorch is an open-source deep learning library developed by Facebook that works with Python and supports tensor operations, automatic differentiation, and GPU acceleration. The advantages of PyTorch include:

  • Support for dynamic computation graphs
  • Intuitive API and thorough documentation
  • Active community and various available examples

3. Deep Learning Learning Process

The deep learning learning process can be broadly divided into four stages: data preparation, model construction, training, and evaluation.

3.1 Data Preparation

To train a deep learning model, data must be prepared. This typically includes the following steps:

  • Data collection
  • Data preprocessing (normalization, sampling, etc.)
  • Separating the training set and testing set

3.2 Preparing Data in PyTorch

In PyTorch, packages like torchvision can be used to handle data. For example, the code to load the CIFAR-10 dataset is as follows:

import torch
import torchvision
import torchvision.transforms as transforms

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

3.3 Model Construction

When constructing a model, the structure of the neural network must be defined. In PyTorch, user-defined models can be created by inheriting the torch.nn.Module class. Below is an example of a simple CNN model:

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

3.4 Model Training

When training a model, a loss function and an optimization algorithm must be defined. Generally, the cross-entropy loss function is used for classification problems, and optimization algorithms such as SGD or Adam can be applied.

import torch.optim as optim

net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(2):  # Repeating the dataset multiple times.
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()  # Initialize gradients
        outputs = net(inputs)  # Forward pass
        loss = criterion(outputs, labels)  # Calculate loss
        loss.backward()  # Backward pass
        optimizer.step()  # Update weights

print('Finished Training')

3.5 Model Evaluation

After training the model, it needs to be evaluated. Typically, the testing dataset is used to calculate accuracy.

correct = 0
total = 0
with torch.no_grad():  # Disable gradient calculation
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

4. Directions for the Advancement of Deep Learning

Deep learning is being utilized in various fields and will continue to evolve. Especially, it is expected to bring innovations in many areas, including autonomous vehicles, medical diagnosis, natural language processing, and image generation. PyTorch will also continue to evolve in line with these trends.

Conclusion

In this article, we started with the basics of deep learning and took a detailed look at the learning process of deep learning using PyTorch. Through the stages of data preparation, model construction, training, and evaluation, we confirmed the various functions and conveniences provided by PyTorch. I hope this guide helps broaden your understanding of deep learning and aids in applying it to real projects.