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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-neural-networks/
课程评论：没有评论
Coursera 课程《Machine Learning: Neural networks from scratch》将带您从零开始，不依赖专用库，用 Python 实现神经网络。课程将深入浅出地讲解神经网络的直观原理和数学基础，揭示训练神经网络必备的关键技巧，如 log-sum-exp trick（稳定训练）和 jacobian-vector product（优化内存）。您将亲手实现各种激活函数（如 ReLU, Softmax）和损失函数（如 MSELoss, NLLLoss），并将训练模型解决实际的图像分类和回归问题。本课程特别适合希望深入理解神经网络工作原理并能从根本上构建神经网络的开发者。具备 Python 基础编程能力是先决条件，一定的代数和分析知识将助您更好地掌握课程内容。课程将持续更新，并加入奖励内容。立即开启您的机器学习之旅！
In this course, we will implement a neural network from scratch, without dedicated libraries. Although we will use the python programming language, at the end of this course, you will be able to implement a neural network in any programming language. We will see how neural networks work intuitively, and then mathematically. We will also see some important tricks, which allow stabilizing the training of neural networks (log-sum-exp trick), and to prevent the memory used during training from growing exponentially (jacobian-vector product). Without these tricks, most neural networks could not be trained. We will train our neural networks on real image classification and regression problems. To do so, we will implement different cost functions, as well as several activation functions. This course is aimed at developers who would like to implement a neural network from scratch as well as those who want to understand how a neural network works from A to Z. This course is taught using the Python programming language and requires basic programming skills. If you do not have the required background, I recommend that you brush up on your programming skills by taking a crash course in programming. It is also recommended that you have some knowledge of Algebra and Analysis to get the most out of this course. Concepts covered: Neural networks Implementing neural networks from scratch Gradient descent and Jacobian matrix The creation of Modules that can be nested in order to create a complex neural architecture The log-sum-exp trick Jacobian vector product Activation functions (ReLU, Softmax, LogSoftmax,...) Cost functions (MSELoss, NLLLoss,...) This course will be frequently updated, with the addition of bonuses. Don't wait any longer before launching yourself into the world of machine learning!
