深度学习必修课：AI算法工程师成长之路
一、深度学习基础体系
1. 核心数学基础
# 自动微分演示(PyTorch)
import torch
x = torch.tensor(2.0, requires_grad=True)
y = x**3 + 3*x**2 - 5*x + 2
y.backward()
print(f"函数值: {y.item()}")
print(f"x=2处的导数: {x.grad.item()}")
关键数学领域：
- 线性代数（矩阵运算、特征分解）
- 概率统计（贝叶斯理论、分布函数）
- 微积分（梯度下降、链式法则）
- 优化理论（凸优化、随机梯度下降）
2. 神经网络基础架构
# 全连接网络实现
import torch.nn as nn
class MLP(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(input_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, hidden_size),
nn.Sigmoid(),
nn.Linear(hidden_size, output_size)
)
def forward(self, x):
return self.layers(x)
# 示例使用
model = MLP(input_size=784, hidden_size=128, output_size=10)
print(model)
二、计算机视觉核心技术
1. CNN经典架构实现
# ResNet残差块实现
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.shortcut = nn.Sequential()
if stride != 1 or in_channels != out_channels:
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1,
stride=stride, bias=False),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
return F.relu(out)
2. 目标检测关键算法
| 算法 | 特点 | 适用场景 |
|---|---|---|
| Faster R-CNN | 两阶段、高精度 | 通用物体检测 |
| YOLOv5 | 单阶段、实时性好 | 视频流分析 |
| RetinaNet | 解决类别不平衡 | 小物体检测 |
| DETR | Transformer架构 | 端到端检测 |
三、自然语言处理进阶
1. Transformer核心实现
# 自注意力机制实现
class SelfAttention(nn.Module):
def __init__(self, embed_size, heads):
super().__init__()
self.embed_size = embed_size
self.heads = heads
self.head_dim = embed_size // heads
self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
self.fc_out = nn.Linear(heads*self.head_dim, embed_size)
def forward(self, values, keys, query, mask):
N = query.shape[0]
value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]
# 分割多头
values = values.reshape(N, value_len, self.heads, self.head_dim)
keys = keys.reshape(N, key_len, self.heads, self.head_dim)
queries = query.reshape(N, query_len, self.heads, self.head_dim)
energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys])
if mask is not None:
energy = energy.masked_fill(mask == 0, float("-1e20"))
attention = torch.softmax(energy / (self.embed_size ** (1/2)), dim=3)
out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape(
N, query_len, self.heads*self.head_dim
)
return self.fc_out(out)
2. 预训练模型应用
# BERT文本分类
from transformers import BertTokenizer, BertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
inputs = tokenizer("This movie was great!", return_tensors="pt")
outputs = model(**inputs)
predictions = torch.argmax(outputs.logits, dim=1)
print(f"预测类别: {predictions.item()}")
四、模型优化与部署
1. 混合精度训练
# AMP自动混合精度
from torch.cuda.amp import autocast, GradScaler
scaler = GradScaler()
for data, target in train_loader:
optimizer.zero_grad()
with autocast():
output = model(data)
loss = criterion(output, target)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
2. ONNX模型导出
# PyTorch转ONNX
dummy_input = torch.randn(1, 3, 224, 224)
torch.onnx.export(
model,
dummy_input,
"resnet18.onnx",
input_names=["input"],
output_names=["output"],
dynamic_axes={
"input": {0: "batch_size"},
"output": {0: "batch_size"}
}
)
五、工业级实战项目
1. 推荐系统双塔模型
# 双塔召回模型
class TwoTower(nn.Module):
def __init__(self, user_dim, item_dim, hidden_dim):
super().__init__()
self.user_tower = nn.Sequential(
nn.Linear(user_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim//2)
)
self.item_tower = nn.Sequential(
nn.Linear(item_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim//2)
)
def forward(self, user_feat, item_feat):
user_embed = F.normalize(self.user_tower(user_feat), p=2, dim=1)
item_embed = F.normalize(self.item_tower(item_feat), p=2, dim=1)
return torch.matmul(user_embed, item_embed.T)
2. 模型服务化部署
# FastAPI模型服务
from fastapi import FastAPI
import torch
from pydantic import BaseModel
app = FastAPI()
model = torch.load("model.pth")
class Request(BaseModel):
data: list
@app.post("/predict")
async def predict(request: Request):
tensor = torch.tensor(request.data)
with torch.no_grad():
output = model(tensor)
return {"prediction": output.tolist()}
六、前沿技术方向
1. 自监督学习
# SimCLR对比学习实现
class SimCLR(nn.Module):
def __init__(self, encoder, projection_dim):
super().__init__()
self.encoder = encoder
self.projector = nn.Sequential(
nn.Linear(encoder.output_dim, 512),
nn.ReLU(),
nn.Linear(512, projection_dim)
)
def forward(self, x1, x2):
z1 = self.projector(self.encoder(x1))
z2 = self.projector(self.encoder(x2))
return F.normalize(z1, dim=1), F.normalize(z2, dim=1)
2. 模型压缩技术对比
| 技术 | 压缩率 | 精度损失 | 硬件支持 |
|---|---|---|---|
| 量化(8-bit) | 4x | <1% | 广泛 |
| 知识蒸馏 | 2-4x | 2-5% | 无要求 |
| 剪枝 | 5-10x | 3-10% | 需要定制 |
| 神经架构搜索 | 自动 | 可优化 | 高成本 |
七、学习路径建议
- 基础阶段(1-3月)：
- Python编程与PyTorch框架
- 经典网络实现(MLP/CNN/RNN)
- 进阶阶段(3-6月)：
- Transformer架构深入
- 大规模预训练模型应用
- 专业方向(6-12月)：
- CV/NLP/推荐系统专精
- 模型部署与优化
- 持续提升：
- 参与Kaggle比赛
- 复现前沿论文
- 开源项目贡献
关键能力培养：
- 数学推导能力
- 工程实现能力
- 论文复现能力
- 业务抽象能力
通过系统学习深度学习核心技术和参与实战项目，开发者可以逐步成长为合格的AI算法工程师。建议在学习过程中：
- 保持每周至少20小时的编码实践
- 建立技术博客记录学习过程
- 参与开源社区和技术分享
- 关注工业界真实业务需求
