Introduction to PyTorch (crash course)

在本课程中，我将以实用且直观的方式解释 PyTorch 的工作原理。我们将超越 API 的使用，这将使您更有信心继续您的机器学习和/或可微分编程之旅。

本课程分为三个部分。

在第一部分，我们将实现（在 Python 中，从头开始）我们自己的可微分编程框架，它与 PyTorch 非常相似。这将使您了解 PyTorch、TensorFlow、JAX 等的工作原理。然后，我们将专注于 PyTorch 并了解基本的张量操作、梯度的计算以及显卡 (GPU) 的使用。

在第二部分中，我们将关注梯度下降算法（对于训练神经网络至关重要）。我们将实现弹道问题的模拟器，并了解如何使用 PyTorch 的强大功能来解决优化问题（对于那些希望的人来说，这个教学问题可以很容易地扩展到实际问题，例如流体力学模拟）。我们还将了解如何使用优化器，以及如何将它们与调度器结合使用以提高效率。

最后，我们将处理神经网络。我们将解决图像分类问题，首先使用 MLP，然后使用 CNN。

Published 07/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 12 lectures (2h 36m) | Size: 812.3 MB

Machine Learning: Introduction to PyTorch, its internal mechanisms and its API

What you’ll learn
How PyTorch works – under the hood
The integrated differentiation engine of PyTorch
Learning PyTorch through practice (tensors, optimizers, schedulers, decorators, …)
Differentiable programming
Solving an optimization problem (“black-box”) with PyTorch
Implementing neural networks with PyTorch

Requirements
Basic programming knowledge

Description
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!

Who this course is for
Anyone who would like to learn PyTorch through practise
Anyone who would like to understand PyTorch in depth
Anyone interested in differentiable programming
Anyone interested in machine learning & artificial intelligence