YouTip

## Common Network Types\n\nImagine we are teaching a child to recognize cats and dogs.\n\nInitially, we would show them many pictures of cats and dogs, telling them "this is a cat" and "this is a dog." Gradually, the child's brain would summarize patterns from these images: cats usually have pointed ears and rounder faces; dogs may have droopy ears and longer faces. This process is essentially **learning features** and **establishing patterns**.\n\n!(#)\n\nDeep learning, as a powerful branch of machine learning, aims to simulate this process with computers. By constructing multi-layer neural networks, machines can automatically learn and extract complex features from massive amounts of data, ultimately completing advanced tasks such as image recognition, language understanding, and trend prediction. Different tasks require different network structures to process. This article will introduce several of the most core and common deep learning network types, helping you understand their design concepts and typical applications.\n\n### Deep Learning Networks\n\n| Chinese Full Name | English Full Name | Abbreviation |\n| --- | --- | --- |\n| Artificial Neural Network | Artificial Neural Network | ANN |\n| Convolutional Neural Network | Convolutional Neural Network | CNN |\n| Recurrent Neural Network | Recurrent Neural Network | RNN |\n| Long Short-Term Memory Network | Long Short-Term Memory | LSTM |\n| Gated Recurrent Unit | Gated Recurrent Unit | GRU |\n| Generative Adversarial Network | Generative Adversarial Network | GAN |\n| Transformer | Transformer | Transformer |\n| Autoencoder | Autoencoder | AE |\n| Variational Autoencoder | Variational Autoencoder | VAE |\n| Deep Belief Network | Deep Belief Network | DBN |\n| Graph Neural Network | Graph Neural Network | GNN |\n\n* * *\n\n## Neural Network Basics and Fully Connected Networks\n\nBefore diving into various network types, we need to understand the most fundamental model—the **fully connected network**, also known as the **multi-layer perceptron**.\n\n### Core Idea: Everything Can Be Connected\n\nThe fully connected network is the most straightforward architecture in deep learning. As the name suggests, **every neuron** in each layer is connected to **every neuron** in the adjacent layer.\n\n!(#)\n\nWe can imagine it as an extremely dense information processing network. Data enters from the input layer, undergoes transformations through multiple hidden layers, and finally produces results from the output layer.\n\n!(#)\n\n### Typical Applications and Limitations\n\n**Applications**: Due to its powerful fitting capability, FCN is very suitable for processing structured data (such as tabular data like house area, location, and number of rooms in house price prediction).\n\n## Example\n\n# A simple fully connected network example (using PyTorch)\n\nimport torch.nn as nn\n\nclass SimpleFCN(nn.Module):\n\ndef __init__ (self, input_size, num_classes):\n\nsuper(SimpleFCN,self). __init__ ()\n\n# Define network layers\n\nself.fc1= nn.Linear(input_size,128)# First hidden layer\n\nself.relu= nn.ReLU()# Activation function\n\nself.fc2= nn.Linear(128,64)# Second hidden layer\n\nself.fc3= nn.Linear(64, num_classes)# Output layer\n\ndef forward(self, x):\n\n x =self.fc1(x)\n\n x =self.relu(x)\n\n x =self.fc2(x)\n\n x =self.relu(x)\n\n x =self.fc3(x)\n\nreturn x\n\n# Assume input is 100-dimensional features, performing 10-class classification\n\n model = SimpleFCN(input_size=100, num_classes=10)\n\n**Limitations**: When processing gridded data such as images and audio, FCN faces significant challenges. Because pixels in images are highly correlated in space, FCN ignores this spatial structure by flattening images into one-dimensional vectors for processing, leading to an explosion in parameter count and difficulty in learning effective spatial features.\n\n* * *\n\n## Convolutional Neural Networks — The Cornerstone of Computer Vision\n\nTo solve image processing problems, **Convolutional Neural Networks** emerged, completely transforming the field of computer vision.\n\n### Core Ideas: Local Perception and Parameter Sharing\n\nCNN's design is inspired by the biological visual cortex. Its two core ideas are:\n\n1. **Local Perception**: Unlike FCN where neurons connect to the entire image, each neuron in CNN only perceives a **small local region** of the image (such as a 3x3 or 5x5 pixel block). This better matches the characteristic that adjacent pixels in images have stronger correlations.\n2. **Parameter Sharing**: The same **convolution kernel** (or filter) is used to slide and scan different positions of the image, extracting the same type of features (such as edges and textures). This greatly reduces network parameters.\n\n!(#)\n\n### Core Components and Typical Applications\n\nA typical CNN consists of the following stacked components:\n\n* **Convolutional Layer**: Uses convolution kernels to extract features.\n* **Pooling Layer** (such as max pooling): Downsamples feature maps to reduce data volume and enhance feature invariance.\n* **Fully Connected Layer**: At the end of the network, maps learned distributed features to sample label space.\n\n!(#)\n\n**Applications**: Image classification, object detection, face recognition, and almost all computer vision tasks.\n\n## Example\n\n# A simple CNN example (for image classification)\n\nclass SimpleCNN(nn.Module):\n\ndef __init__ (self, num_classes=10):\n\nsuper(SimpleCNN,self). __init__ ()\n\nself.conv1= nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1)\n\nself.pool= nn.MaxPool2d(kernel_size=2, stride=2)\n\nself.conv2= nn.Conv2d(16,32,3, padding=1)\n\nself.fc1= nn.Linear(32 * 8 * 8,256)# Assume feature map size is 8x8 after two pooling operations\n\nself.fc2= nn.Linear(256, num_classes)\n\ndef forward(self, x):\n\n x =self.pool(nn.functional.relu(self.conv1(x)))# Convolution -> Activation -> Pooling\n\n x =self.pool(nn.functional.relu(self.conv2(x)))\n\n x = x.view(-1,32 * 8 * 8)# Flatten feature map into one-dimensional vector\n\n x = nn.functional.relu(self.fc1(x))\n\n x =self.fc2(x)\n\nreturn x\n\n* * *\n\n## Recurrent Neural Networks — Experts in Processing Sequential Data\n\nFor data with **sequential dependencies** such as language, speech, and time series, we need a network that can remember historical information—this is the **Recurrent Neural Network**.\n\n!(#)\n\n### Core Idea: Introducing Memory Mechanism\n\nThe core of RNN lies in its recurrent structure. When processing current input, the network combines the current input with the **hidden state from the previous time step** to jointly determine the current output and the hidden state passed to the next time step. This is like when you read a sentence—the meaning of the current word depends on the words you've read before.\n\n!(#)\n\n### Variants and Typical Applications\n\nBasic RNN has long-term dependency problems, making it difficult to learn information in long sequences. Therefore, two important variants emerged:\n\n* **Long Short-Term Memory**: Through sophisticated gating mechanisms (input gate, forget gate, output gate), it selectively remembers important information and forgets useless information, effectively solving long sequence dependency problems.\n* **Gated Recurrent Unit**: A simplified version of LSTM with a more concise structure and higher computational efficiency, performing comparably on many tasks.\n\n**Applications**: Machine translation, text generation, speech recognition, stock price prediction.\n\n## Example\n\n# A simple RNN example (for text sentiment classification)\n\nclass SimpleRNN(nn.Module):\n\ndef __init__ (self, vocab_size, embed_size, hidden_size, num_classes):\n\nsuper(SimpleRNN,self). __init__ ()\n\nself.embedding= nn.Embedding(vocab_size, embed_size)# Word embedding layer\n\nself.rnn= nn.RNN(input_size=embed_size, hidden_size=hidden_size, batch_first=True)\n\nself.fc= nn.Linear(hidden_size, num_classes)\n\ndef forward(self, x):\n\n# x shape: (batch_size, sequence_length)\n\n x =self.embedding(x)# After embedding: (batch_size, seq_len, embed_size)\n\n _, h_n =self.rnn(x)# h_n is the hidden state of the last time step\n\n out =self.fc(h_n.squeeze(0))# Use final state for classification\n\nreturn out\n\n* * *\n\n## Generative Adversarial Networks — From Learning to Creation\n\nIf the previous networks are discriminative models (learning to distinguish data), then **Generative Adversarial Networks** are outstanding representatives of generative models (learning to create data).\n\n!(#)\n\n### Core Idea: Evolution Through Game Theory\n\nGAN's inspiration comes from game theory. It consists of two opposing networks:\n\n* **Generator**: Like a forger, its goal is to learn the distribution of real data and generate new data realistic enough to be mistaken for genuine.\n* **Discriminator**: Like an authentication expert, its goal is to accurately distinguish whether input data comes from the real dataset or the generator.\n\nBoth improve together through continuous adversarial training: the generator strives to create more realistic data to fool the discriminator, while the discriminator works to improve its identification ability. Eventually, the generator can produce high-quality new data.\n\n!(#)\n\n### Typical Applications\n\n**Applications**: Image generation, image super-resolution, style transfer, data augmentation.\n\n## Example\n\n# GAN core training loop pseudocode demonstration\n\nfor epoch in range(num_epochs):\n\n# 1. Train discriminator: maximize ability to identify real data as real and generated data as fake\n\n real_data = get_real_data()\n\n noise = generate_random_noise()\n\n fake_data = generator(noise).detach()# Note detach to prevent generator from being updated\n\nd_loss_real = criterion(discriminator(real_data), real_labels)\n\n d_loss_fake = criterion(discriminator(fake_data), fake_labels)\n\n d_loss = d_loss_real + d_loss_fake\n\n d_loss.backward()\n\n optimizer_D.step()\n\n# 2. Train generator: minimize discriminator's ability to classify generated data as fake (i.e., fool the discriminator)\n\n noise = generate_random_noise()\n\n fake_data = generator(noise)\n\n g_loss = criterion(discriminator(fake_data), real_labels)# Make discriminator think generated is real\n\n g_loss.backward()\n\n optimizer_G.step()\n\n* * *\n\n## Summary and Comparison\n\n| Network Type | Core Idea | Data Types Best Suited For | Typical Applications |\n| --- | --- | --- | --- |\n| **Fully Connected Network** | Global connection, dense fitting | Structured data (tables) | House price prediction, credit scoring |\n| **Convolutional Neural Network** | Local perception, parameter sharing | Gridded data (images) | Image classification, object detection |\n| **Recurrent Neural Network** | Temporal dependency, memory state | Sequential data (text, time series) | Machine translation, speech recognition |\n| **Generative Adversarial Network** | Adversarial game, data generation | For generating data similar to real data | Image generation, style transfer |