the rise of machine learning has been accompanied by a similar rise of tools and libraries to aid machine learning applications. from basic model training with scikit-learn to deep learning...
机器学习的发展依赖于工具和库的进步，如scikit-learn、pytorch和tensorflow。这些框架支持神经网络的创建，通过输入层、隐藏层和输出层进行预测，并使用relu等激活函数进行非线性转换。训练时，模型通过梯度下降和反向传播调整权重。pytorch和tensorflow提供高自定义性和gpu加速。选择合适的库和优化参数是构建有效神经网络的关键。
