课程

使用 PyTorch 的深度学习入门

中级技能水平

更新时间 2026年1月

学习如何在 PyTorch 中构建你的第一个神经网络、调整超参数，并解决分类和回归问题。

免费开始课程

PyTorchArtificial Intelligence

4小时

16 视频

49 道练习

3,900 XP

85,880

成就证明

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课程描述

理解深度学习的强大力量

深度学习无处不在：在智能手机摄像头、语音助手和自动驾驶汽车中。它甚至帮助发现了蛋白质结构，并在围棋比赛中击败了人类。探索这项强大的技术，并学习如何使用 PyTorch（最受欢迎的深度学习库之一）来加以利用。

训练你的第一个神经网络

首先，了解深度学习与“经典”机器学习之间的区别。您将了解神经网络的训练过程，以及如何编写训练循环。为此，你将为回归和分类问题创建损失函数，并利用 PyTorch 计算它们的导数。

评估并改进你的模型

在后半部分，学习可以调整的不同超参数，以提升你的模型。在了解神经网络的不同组成部分后，你将能够创建更大、更复杂的架构。要衡量你的模型性能，你将使用 TorchMetrics，这是一个用于模型评估的 PyTorch 库。

完成后，您将能够利用 PyTorch 使用深度学习解决表格数据和图像数据上的分类与回归问题。对于希望提升职业发展的资深数据专业人士来说，这是一项至关重要的能力。

先决条件

Supervised Learning with scikit-learn Introduction to NumPy Python Toolbox

1

Introduction to PyTorch, a Deep Learning Library

Self-driving cars, smartphones, search engines... Deep learning is now everywhere. Before you begin building complex models, you will become familiar with PyTorch, a deep learning framework. You will learn how to manipulate tensors, create PyTorch data structures, and build your first neural network in PyTorch with linear layers.

Introduction to deep learning with PyTorch

Getting started with PyTorch tensors

Checking and adding tensors

Neural networks and layers

Linear layer network

Understanding weights

Hidden layers and parameters

Your first neural network

Stacking linear layers

Counting the number of parameters

2

Neural Network Architecture and Hyperparameters

To train a neural network in PyTorch, you will first need to understand additional components, such as activation and loss functions. You will then realize that training a network requires minimizing that loss function, which is done by calculating gradients. You will learn how to use these gradients to update your model's parameters.

Discovering activation functions

Activate your understanding!

The sigmoid and softmax functions

Running a forward pass

Building a binary classifier in PyTorch

From regression to multi-class classification

Using loss functions to assess model predictions

Creating one-hot encoded labels

Calculating cross entropy loss

Using derivatives to update model parameters

Accessing the model parameters

Updating the weights manually

Using the PyTorch optimizer

3

Training a Neural Network with PyTorch

Now that you've learned the key components of a neural network, you'll train one using a training loop. You'll explore potential issues like vanishing gradients and learn strategies to address them, such as alternative activation functions and tuning learning rate and momentum.

A deeper dive into loading data

Using TensorDataset

Using DataLoader

Writing our first training loop

Using the MSELoss

Writing a training loop

ReLU activation functions

Implementing ReLU

Implementing leaky ReLU

Understanding activation functions

Learning rate and momentum

Experimenting with learning rate

Experimenting with momentum

4

Evaluating and Improving Models

Training a deep learning model is an art, and to make sure our model is trained correctly, we need to keep track of certain metrics during training, such as the loss or the accuracy. We will learn how to calculate such metrics and how to reduce overfitting.

Layer initialization and transfer learning

Fine-tuning process

Freeze layers of a model

Layer initialization

Evaluating model performance

Writing the evaluation loop

Calculating accuracy using torchmetrics

Fighting overfitting

Experimenting with dropout

Understanding overfitting

Improving model performance

Implementing random search

Wrap-up video

使用 PyTorch 的深度学习入门

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