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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-pytorch/
课程评论：没有评论
课程名称：PyTorch入门（速成课程） 课程概述：本课程以实用和直观的方式讲解PyTorch的工作原理，超越API的使用，使您在机器学习和/或可微编程的旅程中更加自信。课程分为三个部分。第一部分，我们将从零开始使用Python实现一个类似于PyTorch的可微编程框架，这将帮助您理解PyTorch、TensorFlow、JAX等是如何工作的。接着，我们将重点关注PyTorch，学习基本的张量操作、梯度计算和图形处理单元（GPU）的使用。 第二部分，我们将专注于梯度下降算法（训练神经网络的关键）。我们将实现一个弹道问题的模拟器，利用PyTorch的强大功能来解决优化问题（这个教学问题可以扩展到真实场景，例如流体力学模拟，适合有兴趣的学员）。我们还将学习如何使用优化器并将其与调度器结合，以提高效率。 最后，我们将探讨神经网络。我们将解决一个图像分类问题，首先使用多层感知机（MLP），然后用卷积神经网络（CNN）进行处理。 如果这个课程吸引了您，别犹豫，立即加入吧！
In this course, I will explain in a practical and intuitive way how PyTorch works. We will go beyond the use of the API which will allow you to continue your journey in machine learning and/or differentiable programming with more confidence. This course is divided into three parts.In the first part, we will implement (in Python, from scratch) our own differentiable programming framework, which will be very similar to PyTorch. This will allow you to understand how PyTorch, TensorFlow, JAX, etc. work. Then, we will focus on PyTorch and see the basic tensor operations, the calculation of gradients and the use of graphics cards (GPUs).In the second part, we will focus on gradient descent algorithms (essential for training neural networks). We will implement the simulator of a ballistic problem and see how to use the power of PyTorch to solve an optimization problem (this pedagogical problem can be easily extended to real problems, such as fluid mechanics simulations, for those who wish). We will also see how to use optimizers and how to combine them with schedulers to make them even more efficient.Finally, we will tackle neural networks. We will solve an image classification problem, first with an MLP, and then with a CNN.If this program enchants you, don't wait any longer!
