1. What is Deep Learning?
Deep Learning is a field of machine learning based on artificial neural networks, which learns patterns from data to make predictions. Inspired by the structure of the human brain, deep learning employs multi-layer neural networks to understand and learn from input data through appropriate nonlinear transformations. It is utilized in various fields such as image recognition, natural language processing, and speech recognition.
2. What is PyTorch?
PyTorch is an open-source machine learning library developed by Facebook’s artificial intelligence research team. Using PyTorch, one can easily implement the process of constructing and training deep learning models, and it supports dynamic computation graphs, allowing for more intuitive model development. PyTorch is primarily written in Python and enables high-speed computations using GPUs.
3. Key Terms in Deep Learning
-
3.1 Artificial Neural Network (ANN)
An artificial neural network is a model developed based on the structure of biological neural networks. It consists of multiple layers, each processing input signals and passing them to the next layer.
-
3.2 Loss Function
The loss function measures the difference between the predicted and actual values of the model. A lower value of the loss function indicates better model performance.
-
3.3 Backpropagation
Backpropagation is an algorithm used in neural networks to update weights in order to minimize the loss function. It adjusts the weights using gradient descent.
-
3.4 Overfitting
Overfitting is a phenomenon where the model fits the training data too well, resulting in poor generalization performance on new data. Regularization techniques are used to prevent this.
-
3.5 Hyperparameter
Hyperparameters are parameters that must be set during the model training process, such as learning rate and batch size. The choice of hyperparameters can significantly affect the model’s performance.
4. PyTorch Example Code
4.1 Constructing a Basic Artificial Neural Network
The following is code that uses PyTorch to construct a basic artificial neural network and train it on the MNIST digit recognition dataset.
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# Load dataset
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
# Define artificial neural network class
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 28 * 28) # Convert to 1D
x = torch.relu(self.fc1(x)) # Activation function
x = self.fc2(x) # Final output
return x
# Define model, loss function and optimizer
model = SimpleNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Training
for epoch in range(5): # Training for 5 epochs
for images, labels in train_loader:
optimizer.zero_grad() # Initialize gradients
outputs = model(images) # Model prediction
loss = criterion(outputs, labels) # Calculate loss
loss.backward() # Backpropagation
optimizer.step() # Update weights
print(f'Epoch {epoch+1}, Loss: {loss.item()}')
This code is an example of training a very simple artificial neural network model to classify handwritten digits from the MNIST dataset. The processes of loss calculation, backpropagation, and weight updates occur throughout the construction of the neural network.
5. Future Directions of Deep Learning
Deep learning has rapidly advanced in recent years, particularly demonstrating remarkable achievements in the fields of natural language processing and image processing. Technologies such as Transformer models, GANs (Generative Adversarial Networks), and Deep Reinforcement Learning are establishing themselves as cutting-edge technologies for the future and can be applied across various industries. Moreover, research on efficient resource use, environmentally friendly learning, and model lightweighting is being actively pursued.