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基于Python语言和PyTorch深度学习框架完成的一个猫狗分类实验。 深度学习(Deep Learning,简称DL)是机器学习(Machine Learning,简称ML)领域中一个新的研究方向,其目标是让机器能够像人一样具有分析学习能力,识别文字、图像和声音等数据。深度学习通过学习样本数据的内在规律和表示层次,使机器能够模仿视听和思考等人类活动,从而解决复杂的模式识别难题。 深度学习的核心是神经网络,它由若干个层次构成,每个层次包含若干个神经元。神经元接收上一层次神经元的输出作为输入,通过加权和转换后输出到下一层次神经元,最终生成模型的输出结果。神经网络之间的权值和偏置是神经网络的参数,决定了输入值和输出值之间的关系。 深度学习的训练过程通常涉及反向传播算法,该算法用于优化网络参数,使神经网络能够更好地适应数据。训练数据被输入到神经网络中,通过前向传播算法将数据从输入层传递到输出层,然后计算网络输出结果与实际标签之间的差异,即损失函数。通过反向传播算法,网络参数会被调整以减小损失函数值,直到误差达到一定的阈值为止。 深度学习中还包含两种主要的神经网络类型:卷积神经网络(C
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基于Python语言和PyTorch深度学习框架完成的一个猫狗分类实验 (2000个子文件)
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