【AIGC】AI、大数据、机器学习、深度学习、神经网络之间的关系详解：你必须知道的五个关键点！ 原创

人工智能 (AI)
    ├── 机器学习
    │   └── 深度学习
    │       └── 神经网络
    └── 大数据
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def analyze_data_scale_impact():
    data_sizes = [1000, 10000, 100000, 1000000]
    performance_metrics = []
    
    for size in data_sizes:
        # 1. 准备数据
        data = load_data(size)
        
        # 2. 训练模型
        model = train_model(data)
        
        # 3. 评估性能
        metrics = evaluate_model(model)
        performance_metrics.append(metrics)
    
    return analyze_correlation(data_sizes, performance_metrics)
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class DeepNeuralNetwork(nn.Module):
    def __init__(self, input_size, hidden_sizes, output_size):
        super().__init__()
        
        # 构建多层神经网络
        layers = []
        prev_size = input_size
        
        # 添加隐藏层
        for size in hidden_sizes:
            layers.extend([
                nn.Linear(prev_size, size),
                nn.ReLU(),
                nn.BatchNorm1d(size),
                nn.Dropout(0.5)
            ])
            prev_size = size
        
        # 添加输出层
        layers.append(nn.Linear(prev_size, output_size))
        
        self.network = nn.Sequential(*layers)
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def choose_technology(task_requirements):
    if task_requirements['data_size'] < 1000:
        return'traditional_ai'
    
    elif task_requirements['interpretability_needed']:
        return'machine_learning'
    
    elif task_requirements['data_size'] > 100000and \
         task_requirements['computational_resources'] == 'high':
        return'deep_learning'
    
    else:
        return'hybrid_approach'
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class ImageRecognitionSystem:
    def __init__(self):
        # 1. 数据处理（大数据）
        self.data_pipeline = DataPipeline()
        
        # 2. 深度学习模型（基于神经网络）
        self.model = ConvolutionalNeuralNetwork()
        
        # 3. 机器学习评估系统
        self.evaluator = ModelEvaluator()
    
    def train(self, data_source):
        # 大数据处理
        processed_data = self.data_pipeline.process(data_source)
        
        # 深度学习训练
        self.model.train(processed_data)
        
        # 性能评估
        metrics = self.evaluator.evaluate(self.model)
        return metrics
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class NLPSystem:
    def __init__(self):
        # 1. 预训练语言模型（深度学习）
        self.language_model = TransformerModel()
        
        # 2. 任务特定模型（机器学习）
        self.task_model = TaskSpecificModel()
        
        # 3. 规则引擎（传统AI）
        self.rule_engine = RuleEngine()
    
    def process_text(self, text):
        # 分步处理
        embeddings = self.language_model.encode(text)
        predictions = self.task_model.predict(embeddings)
        refined_results = self.rule_engine.refine(predictions)
        
        return refined_results
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