From CNN → RNN → Transformer → BERT → GPT Explain the "development logic of the large model" completely clearly.
The following teaching sequence compares several architectures with overlapping development; it is not a strict historical or replacement chain.
1. Understanding local features through CNNs
Representative paper: ImageNet Classification with Deep Convolutional Neural Networks
Author: Alex Krizhevsky et al.
1️⃣What does CNN do?
CNN is mainly used for:
image recognition
For example:
- Identify cats and dogs
- face recognition
- medical image analysis
2️⃣ Core idea
The key to CNN is:
Convolution
Simple understanding:
- Use a "little window" to slide over the image
- Extract local features
- Combined layer by layer into higher-level features
For example:
- The first layer recognizes edges
- The second layer recognizes shapes
- The third layer recognizes objects
3️⃣ Tradeoffs when using CNNs for language
CNNs are not restricted to two-dimensional images; one-dimensional convolutions can model language sequences. Local receptive fields can be expanded through stacking or dilated convolutions, so being sequential does not make language incompatible with CNNs.
For example, Convolutional Sequence to Sequence Learning models sequences with convolutions. The relevant comparison concerns dependency modeling and computation.
2. Sequence Era: RNN (Recurrent Neural Network)
1️⃣What problem does RNN solve?
RNN solves:
Language is a time series
It can "remember the previous words."
For example:
"I didn't bring an umbrella today because ____"
RNN will remember "no umbrella".
2️⃣The core mechanism of RNN
Every step:
当前输入 + 上一步的记忆 → 当前状态It is like a "network that transmits memories."
3️⃣ But RNN has two serious problems
❌ 1. Memory will decay (gradient disappears)
I can’t remember sentences that are too long.
❌ 2. Sequential dependencies between time steps
Traditional RNN time steps depend on previous states, while batches and other dimensions can still run in parallel.
Training is very slow.
Later there were improvements:
- LSTM
- GRU
LSTM/GRU mitigate some long-term dependency problems, while retaining sequential computation and optimization costs; they do provide improvements.
3. Revolution: Transformer
Representative paper: Attention Is All You Need
Author from: Google
This paper changed everything.
1️⃣What does Transformer solve?
Two core breakthroughs:
✅ No more using RNN
✅ Use Attention mechanism
2️⃣ What is Attention?
In one sentence:
Self-attention relates tokens within the allowed mask; bidirectional encoders can use both directions, while causal decoders cannot see future positions.
For example:
"I didn't bring an umbrella today because the weather was nice."
"No umbrella" will focus on "the weather is good".
This is it:
Modeling long-range dependencies
3️⃣ Advantages of Transformer
| Features | RNN | Transformer |
|---|---|---|
| Long distance dependencies | Poor | Strong |
| Parallelism | Sequential dependencies across time steps | Training can parallelize positions; autoregressive generation remains sequential |
| Training speed | Slow | Fast |
| Scalability | Weak | Extremely strong |
Transformer becomes the basis for large models.
4. BERT: Two-way understanding model
Representative paper: BERT: Pre-training of Deep Bidirectional Transformers
1️⃣ What is BERT?
BERT is:
Transformer-based “understanding model”
It is:
Encoder-only model
2️⃣ What is two-way?
GPT can only be read from left to right:
I am very…
BERT is bidirectional:
Look at the left and right simultaneously
For example:
“The apples are delicious”
BERT will determine whether "apple" is a fruit or a company based on the context.
3️⃣ What is BERT good at?
- Text classification
- sentiment analysis
- Search sorting
- question and answer system
But:
❌ Not good at generating articles
5. GPT: The rise of generative models
GPT representative series:
- GPT-3
- GPT-4 From: OpenAI
1️⃣What is GPT?
GPT is:
Generative model based on Transformer Decoder
It is:
Auto-regressive model
means:
Prediction word by word backward
2️⃣ The difference between GPT and BERT
| Comparison | BERT | GPT |
|---|---|---|
| Structure | Encoder | Decoder |
| Is it bidirectional | Yes | No |
| Good at | Understanding | Generating |
| Typical tasks | Classification | Writing |
3️⃣ Why can GPT become a “big model”?
Three reasons:
1️⃣ Extremely scalable
Scale depends on the specific architecture; undisclosed parameter counts must not be treated as known facts:
- Smaller models may have hundreds of millions of parameters
- The largest GPT-3 version disclosed in its paper has 175 billion parameters
- For other models, distinguish total and active parameters and cite public reports
2️⃣ Pre-training + fine-tuning
Learn the common language first Do human alignment again
3️⃣ Scaling Law
Scaling can improve performance under particular data, training-budget, and evaluation conditions; more parameters do not guarantee better results on every task.
6. Evolutionary logic from CNN to GPT
The following is a usage overview, not an order of invention or a restriction to the listed tasks:
CNN → 图像理解
RNN → 序列建模
Transformer → 全局注意力
BERT → 强理解
GPT → 强生成The essential changes are:
From the "Task Specific Model" to "Universal Language Model"
7. What is the essence of today’s large models?
Modern large model =
Transformer架构
+ 海量数据预训练
+ 超大参数规模
+ 人类反馈对齐It's not a simple "chatbot".
It is:
A general reasoning engine in language space.
8. Summarize each stage in one sentence
CNN：
Learn to read pictures
RNN：
Learn to memorize sequences
Transformer：
Learn to understand the overall situation
BERT：
Learn to understand text deeply
GPT：
Learn to generate the world