From Traditional Machine Learning to Deep Learning: A Comprehensive Guide to Algorithms, Feature Engineering, and Model Training

Introduction

The piece begins by explaining why large language models are popular, then asks what algorithms preceded them and how they evolved toward AGI, setting the stage for a deep dive into machine‑learning fundamentals.

Traditional Machine Learning

It describes linear models (e.g., linear regression, logistic regression), the role of feature engineering (one‑hot, embedding, binning), and shows a concrete scoring table for evaluating Mini‑Program quality. The mathematical formulation of a weighted sum and the use of sigmoid for binary classification are illustrated.

Model Training and Evaluation

Key steps such as preparing samples, splitting data into training/validation/test sets, selecting hyper‑parameters (epochs, batch size, learning rate, optimizer), and monitoring loss are covered. Common metrics—accuracy, precision, recall, F1‑score, MCC, and AUC—are explained with confusion‑matrix examples.

Code Example

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from torch.utils.data import DataLoader, TensorDataset

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

data = pd.read_csv('mini_program_data.csv')
X = data[['completeness', 'error_rate']].values
y = data['label'].values

scaler = StandardScaler()
X = scaler.fit_transform(X)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

X_train_tensor = torch.tensor(X_train, dtype=torch.float32).to(device)
y_train_tensor = torch.tensor(y_train, dtype=torch.float32).view(-1, 1).to(device)
X_val_tensor = torch.tensor(X_val, dtype=torch.float32).to(device)
y_val_tensor = torch.tensor(y_val, dtype=torch.float32).view(-1, 1).to(device)

train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

class LogisticRegressionModel(nn.Module):
    def __init__(self, input_dim):
        super(LogisticRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, 1)
    def forward(self, x):
        return torch.sigmoid(self.linear(x))

model = LogisticRegressionModel(input_dim=X_train.shape[1]).to(device)
criterion = nn.BCELoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

for epoch in range(100):
    model.train()
    for batch_X, batch_y in train_loader:
        optimizer.zero_grad()
        outputs = model(batch_X)
        loss = criterion(outputs, batch_y)
        loss.backward()
        optimizer.step()
    if (epoch + 1) % 10 == 0:
        print(f'Epoch [{epoch+1}/100], Loss: {loss.item():.4f}')

model.eval()
with torch.no_grad():
    val_outputs = model(X_val_tensor)
    predicted = (val_outputs > 0.5).float()
    # accuracy and F1 would be computed here

Deep Learning and MLP

The article introduces artificial neural networks, explains why linear models are limited, and presents the Multi‑Layer Perceptron (MLP) as a universal approximator. It covers activation functions (sigmoid, ReLU, Leaky ReLU, GELU, Swish) and the universal approximation theorem, showing how MLPs can handle regression, binary and multi‑class tasks.

Transformer and Recommendation Models

Self‑attention, multi‑head attention, positional encoding, and masked attention are described, leading to the transformer architecture. Building on this, modern recommendation models such as Wide&Deep, DeepFM, DCN, DCN‑V2, DIN, and sequence‑based approaches (GRU4Rec, BERT4Rec, BST) are outlined, highlighting how they combine feature engineering, graph embeddings, and temporal modeling.

Conclusion

Finally, the article summarizes key takeaways: embedding + MLP is a powerful paradigm, improving embeddings and model capacity yields better performance, and integrating knowledge graphs, temporal features, and appropriate loss functions can further boost recommendation quality.