2023 年 PYTORCH[特殊字符]深入课程

Chapter Index:¶

Chapter 1: Introduction to PyTorch

• 1.1 What is PyTorch?
• 1.2 Advantages of PyTorch over other libraries
• 1.3 Installing PyTorch
• 1.4 PyTorch Tensors

Chapter 2: PyTorch Basics

• 2.1 Creating and manipulating tensors
• 2.2 Tensor operations and functions
• 2.3 Automatic differentiation with autograd

Chapter 3: PyTorch Datasets and Data Loaders

• 3.1 Working with datasets in PyTorch
• 3.2 Creating custom datasets
• 3.3 Loading data with data loaders

Chapter 4: Building Neural Networks in PyTorch

• 4.1 Creating neural network architectures
• 4.2 Defining forward and backward passes
• 4.3 Training neural networks

Chapter 5: Loss Functions and Optimization

• 5.1 Common loss functions in PyTorch
• 5.2 Optimizers and parameter updates
• 5.3 Learning rate scheduling

Chapter 6: Model Training and Evaluation

• 6.1 Splitting data into train and test sets
• 6.2 Training loops and batch processing
• 6.3 Evaluating model performance

Chapter 7: Transfer Learning in PyTorch

• 7.1 Understanding transfer learning
• 7.2 Pretrained models in PyTorch
• 7.3 Fine-tuning and feature extraction

Chapter 8: Convolutional Neural Networks (CNNs)

• 8.1 Introduction to CNNs
• 8.2 Building CNN architectures in PyTorch
• 8.3 Training and evaluating CNNs

Chapter 9: Recurrent Neural Networks (RNNs)

• 9.1 Introduction to RNNs
• 9.2 Building RNN architectures in PyTorch
• 9.3 Training and evaluating RNNs

Chapter 10: Generative Adversarial Networks (GANs)

• 10.1 Introduction to GANs
• 10.2 Building GAN architectures in PyTorch
• 10.3 Training and generating samples with GANs

Chapter 11: Reinforcement Learning with PyTorch

• 11.1 Introduction to reinforcement learning
• 11.2 Building RL agents in PyTorch
• 11.3 Training and evaluating RL agents

Chapter 12: Model Deployment with PyTorch

• 12.1 Saving and loading PyTorch models
• 12.2 Deploying PyTorch models in production
• 12.3 Model optimization and quantization

Chapter 13: Advanced PyTorch Techniques

• 13.1 Custom loss functions and metrics
• 13.2 Model interpretation and visualization
• 13.3 Handling imbalanced datasets

Chapter 14: PyTorch on GPUs

• 14.1 Utilizing GPUs with PyTorch
• 14.2 Data parallelism and distributed training

Chapter 15: PyTorch and ONNX

• 15.1 Introduction to ONNX
• 15.2 Converting PyTorch models to ONNX
• 15.3 Deploying ONNX models in different frameworks

Chapter 16: PyTorch and TensorBoard

• 16.1 Visualizing training progress with TensorBoard
• 16.2 Logging metrics and visualizing network graphs

Chapter 17: PyTorch and Deep Learning Libraries (continued)

• 17.1 Integrating PyTorch with TensorFlow
• 17.2 PyTorch and Keras interoperability
• 17.3 PyTorch and scikit-learn integration

Chapter 18: Model Interpretability and Explainability

• 18.1 Interpreting PyTorch models with Captum
• 18.2 Feature importance and attribution methods
• 18.3 Explaining model predictions

Chapter 19: Handling Time Series Data with PyTorch

• 19.1 Time series data preprocessing
• 19.2 Building recurrent models for time series forecasting
• 19.3 Evaluating and improving time series models

Chapter 20: Natural Language Processing with PyTorch

• 20.1 Text preprocessing and tokenization
• 20.2 Building text classification models
• 20.3 Sequence-to-sequence models for machine translation

Chapter 21: Autoencoders and Variational Autoencoders

• 21.1 Introduction to autoencoders
• 21.2 Building autoencoder architectures in PyTorch
• 21.3 Variational autoencoders for generative modeling

Chapter 22: Reinforcement Learning in Robotics with PyTorch

• 22.1 Reinforcement learning in robotics applications
• 22.2 Building RL agents for robotic tasks
• 22.3 Simulation and real-world deployment

Chapter 23: PyTorch and Bayesian Deep Learning

• 23.1 Introduction to Bayesian deep learning
• 23.2 Bayesian neural networks with PyTorch
• 23.3 Uncertainty estimation and model calibration

Chapter 24: Time Series Analysis with Deep Learning

• 24.1 Time series forecasting with deep learning models
• 24.2 Long Short-Term Memory (LSTM) networks
• 24.3 Temporal convolutional networks (TCN)

Chapter 25: PyTorch and Graph Neural Networks

• 25.1 Introduction to graph neural networks (GNNs)
• 25.2 Building GNN architectures in PyTorch
• 25.3 Node classification and graph representation learning

Chapter 26: Federated Learning with PyTorch

• 26.1 Introduction to federated learning
• 26.2 Implementing federated learning with PyTorch
• 26.3 Privacy and security considerations

Chapter 27: Deep Reinforcement Learning with PyTorch

• 27.1 Deep Q-Networks (DQN) and value-based methods
• 27.2 Policy gradients and actor-critic methods
• 27.3 Combining RL and deep learning techniques

Chapter 28: PyTorch and Computer Vision Applications

• 28.1 Object detection with PyTorch
• 28.2 Image segmentation using convolutional networks
• 28.3 Facial recognition and emotion detection

Chapter 29: Time Series Forecasting with Transformers

• 29.1 Introduction to transformer models
• 29.2 Applying transformers for time series forecasting
• 29.3 Transformer-based sequence-to-sequence models

Chapter 30: PyTorch for Natural Language Generation

• 30.1 Language modeling with recurrent and transformer models
• 30.2 Text generation and chatbots with PyTorch
• 30.3 Neural machine translation with transformers

Chapter 31: Adversarial Attacks and Defenses in Deep Learning (continued)

• 31.1 Generating adversarial examples with PyTorch
• 31.2 Defending against adversarial attacks

Chapter 32: PyTorch Model Compression and Quantization

• 32.1 Techniques for model compression
• 32.2 Quantization and reducing model size
• 32.3 Pruning and sparsity in PyTorch models

Chapter 33: PyTorch and AutoML

• 33.1 Introduction to AutoML
• 33.2 Automated neural architecture search
• 33.3 Hyperparameter optimization with PyTorch

Chapter 34: PyTorch for Time Series Anomaly Detection

• 34.1 Anomaly detection techniques for time series data
• 34.2 Building anomaly detection models with PyTorch
• 34.3 Evaluating and interpreting anomaly detection results

Chapter 35: PyTorch for Recommender Systems

• 35.1 Introduction to recommender systems
• 35.2 Collaborative filtering with PyTorch
• 35.3 Building neural network-based recommenders

Chapter 36: Advanced Optimization Methods in PyTorch

• 36.1 Stochastic gradient descent variants
• 36.2 Adaptive optimization algorithms
• 36.3 Second-order optimization methods

Chapter 37: PyTorch and Knowledge Graphs

• 37.1 Introduction to knowledge graphs
• 37.2 Representing and embedding knowledge graphs with PyTorch
• 37.3 Graph-based reasoning and link prediction

Chapter 38: Explainable AI with PyTorch

• 38.1 Interpreting black-box models with LIME and SHAP
• 38.2 Building interpretable models with PyTorch
• 38.3 Explainable AI in real-world applications

Chapter 39: PyTorch for Time Series Classification

• 39.1 Introduction to time series classification
• 39.2 Feature extraction and representation learning
• 39.3 Building classifiers for time series data

Chapter 40: PyTorch and Bayesian Optimization

• 40.1 Introduction to Bayesian optimization
• 40.2 Implementing Bayesian optimization with PyTorch
• 40.3 Hyperparameter tuning and optimization

Chapter 41: PyTorch for Audio Processing

• 41.1 Audio data preprocessing and feature extraction
• 41.2 Building audio classification models
• 41.3 Speech recognition and synthesis with PyTorch

Chapter 42: PyTorch and Reinforcement Learning in Robotics

• 42.1 Combining PyTorch with robotics frameworks
• 42.2 Reinforcement learning for robot control
• 42.3 Sim-to-real transfer in robotics applications

Chapter 43: PyTorch for Time Series Clustering

• 43.1 Introduction to time series clustering
• 43.2 Feature extraction and representation learning
• 43.3 Clustering algorithms for time series data

Chapter 44: PyTorch and Natural Language Understanding

• 44.1 Text classification and sentiment analysis
• 44.2 Named entity recognition and information extraction
• 44.3 Natural language understanding applications

Chapter 45: PyTorch for Image Captioning

• 45.1 Introduction to image captioning
• 45.2 Building image captioning models with PyTorch
• 45.3 Generating captions for images

Chapter 46: PyTorch and Graph Representation Learning (continued)

• 46.1 Graph embedding techniques with PyTorch
• 46.2 Graph neural networks for graph representation learning
• 46.3 Link prediction and node classification with GNNs

Chapter 47: PyTorch for Object Detection

• 47.1 Introduction to object detection
• 47.2 Building object detection models with PyTorch
• 47.3 Training and evaluation for object detection

Chapter 48: PyTorch for Time Series Segmentation

• 48.1 Introduction to time series segmentation
• 48.2 Segmentation methods for time series data
• 48.3 Building segmentation models with PyTorch

Chapter 49: PyTorch and Meta-Learning

• 49.1 Introduction to meta-learning
• 49.2 Building meta-learning models with PyTorch
• 49.3 Few-shot learning and adaptation

Chapter 50: PyTorch for Anomaly Detection

• 50.1 Introduction to anomaly detection
• 50.2 Building anomaly detection models with PyTorch
• 50.3 Outlier detection and novelty detection

Chapter 51: PyTorch for Multi-Task Learning

• 51.1 Introduction to multi-task learning
• 51.2 Building multi-task learning models with PyTorch
• 51.3 Joint training and task-specific outputs

Chapter 52: PyTorch and Graph Generation

• 52.1 Graph generation techniques with PyTorch
• 52.2 Graph autoencoders and variational graph models
• 52.3 Generating graphs with desired properties

Chapter 53: PyTorch for Time Series Forecasting with Attention Mechanisms

• 53.1 Attention mechanisms in time series forecasting
• 53.2 Building attention-based models with PyTorch
• 53.3 Interpreting attention weights

Chapter 54: PyTorch for Video Processing

• 54.1 Video data preprocessing and transformation
• 54.2 Building video classification models with PyTorch
• 54.3 Action recognition and video generation

Chapter 55: PyTorch and Semi-Supervised Learning

• 55.1 Introduction to semi-supervised learning
• 55.2 Building semi-supervised models with PyTorch
• 55.3 Leveraging unlabeled data for improved performance

Chapter 56: PyTorch for Out-of-Distribution Detection

• 56.1 Out-of-distribution detection techniques
• 56.2 Building OOD detection models with PyTorch
• 56.3 Evaluating and benchmarking OOD detection methods

Chapter 57: PyTorch and Model Ensemble Techniques

• 57.1 Ensemble methods in deep learning
• 57.2 Building model ensembles with PyTorch
• 57.3 Combining multiple models for improved performance

Chapter 58: PyTorch for Explainable Recommendation Systems

• 58.1 Interpretable models for recommendation systems
• 58.2 Explainable recommendation methods with PyTorch
• 58.3 User modeling and personalization

Chapter 59: PyTorch for Time Series Imputation

• 59.1 Missing data imputation techniques for time series
• 59.2 Building imputation models with PyTorch
• 59.3 Handling missing values in time series data

Chapter 60: PyTorch and Continual Learning (continued)

• 60.1 Introduction to continual learning
• 60.2 Building continual learning models with PyTorch
• 60.3 Catastrophic forgetting and regularization techniques

Chapter 61: PyTorch for Image Style Transfer

• 61.1 Introduction to image style transfer
• 61.2 Building style transfer models with PyTorch
• 61.3 Transferring styles between images

Chapter 62: PyTorch and Hyperparameter Optimization Libraries

• 62.1 Introduction to hyperparameter optimization
• 62.2 Integrating PyTorch with hyperparameter optimization libraries
• 62.3 Efficient hyperparameter search strategies

Chapter 63: PyTorch for Music Generation

• 63.1 Music data preprocessing and representation
• 63.2 Building music generation models with PyTorch
• 63.3 Creating original music compositions

Chapter 64: PyTorch and Federated Learning in Healthcare

• 64.1 Applications of federated learning in healthcare
• 64.2 Building privacy-preserving models with PyTorch
• 64.3 Collaborative learning across distributed healthcare datasets

Chapter 65: PyTorch for Image Super-Resolution

• 65.1 Introduction to image super-resolution
• 65.2 Building super-resolution models with PyTorch
• 65.3 Upsampling and enhancing image details

Chapter 66: PyTorch and Self-Supervised Learning

• 66.1 Introduction to self-supervised learning
• 66.2 Pretext tasks and self-supervised models
• 66.3 Transferring learned representations to downstream tasks

Chapter 67: PyTorch for StyleGAN and Image Synthesis

• 67.1 Introduction to StyleGAN and image synthesis
• 67.2 Building StyleGAN models with PyTorch
• 67.3 Generating high-quality synthetic images

Chapter 68: PyTorch for Active Learning

• 68.1 Introduction to active learning
• 68.2 Building active learning workflows with PyTorch
• 68.3 Query strategies and uncertainty sampling

Chapter 69: PyTorch for Time Series Anomaly Detection with Transformers

• 69.1 Introduction to transformer-based anomaly detection
• 69.2 Building transformer-based models with PyTorch
• 69.3 Detecting anomalies in time series data

Chapter 70: PyTorch and Continual Reinforcement Learning

• 70.1 Continual reinforcement learning setups and challenges
• 70.2 Building continual RL agents with PyTorch
• 70.3 Balancing exploration and exploitation in continual RL

Chapter 71: PyTorch for 3D Object Detection

• 71.1 Introduction to 3D object detection
• 71.2 Building 3D object detection models with PyTorch
• 71.3 Training and evaluating on 3D datasets

Chapter 72: PyTorch and Active Vision

• 72.1 Introduction to active vision
• 72.2 Building active vision models with PyTorch
• 72.3 Active perception and information gain

Chapter 73: PyTorch for Few-Shot Object Detection

• 73.1 Introduction to few-shot object detection
• 73.2 Building few-shot detection models with PyTorch
• 73.3 Adaptation and generalization to unseen classes

Chapter 74: PyTorch for Tabular Data Analysis (continued)

• 74.1 Preparing tabular data for modeling with PyTorch
• 74.2 Building deep learning models for tabular data
• 74.3 Feature engineering and feature selection

Chapter 75: PyTorch and Weakly Supervised Learning

• 75.1 Introduction to weakly supervised learning
• 75.2 Building weakly supervised models with PyTorch
• 75.3 Label noise and learning with incomplete annotations

Chapter 76: PyTorch for Text Style Transfer

• 76.1 Introduction to text style transfer
• 76.2 Building text style transfer models with PyTorch
• 76.3 Transferring the style of text

Chapter 77: PyTorch and Graph Representation Learning with Transformers

• 77.1 Graph transformer networks for graph representation learning
• 77.2 Building graph transformer models with PyTorch
• 77.3 Graph classification and graph generation

Chapter 78: PyTorch for Video Super-Resolution

• 78.1 Introduction to video super-resolution
• 78.2 Building video super-resolution models with PyTorch
• 78.3 Enhancing video quality and details

Chapter 79: PyTorch and Continual Learning for Natural Language Processing

• 79.1 Continual learning setups for NLP tasks
• 79.2 Building continual learning models for NLP with PyTorch
• 79.3 Adapting to new tasks while retaining previous knowledge

Chapter 80: PyTorch for Synthetic Data Generation

• 80.1 Generating synthetic data with GANs in PyTorch
• 80.2 Applications of synthetic data generation
• 80.3 Evaluating and utilizing synthetic data

Chapter 81: PyTorch for Few-Shot Natural Language Understanding

• 81.1 Introduction to few-shot NLU
• 81.2 Building few-shot NLU models with PyTorch
• 81.3 Learning from limited labeled examples

Chapter 82: PyTorch for Knowledge Distillation

• 82.1 Introduction to knowledge distillation
• 82.2 Distilling knowledge from large models to smaller models
• 82.3 Model compression and performance trade-offs

Chapter 83: PyTorch for GAN Inversion

• 83.1 GAN inversion techniques in PyTorch
• 83.2 Reconstructing latent representations from generated samples
• 83.3 Applications in image editing and style transfer

Chapter 84: PyTorch for Speech Enhancement and Separation

• 84.1 Introduction to speech enhancement and separation
• 84.2 Building speech enhancement models with PyTorch
• 84.3 Separating speech sources from mixed audio

Chapter 85: PyTorch and Meta-Transfer Learning

• 85.1 Meta-transfer learning for few-shot learning
• 85.2 Building meta-transfer learning models with PyTorch
• 85.3 Transferring knowledge across related tasks

Chapter 86: PyTorch for Cross-Modal Learning

• 86.1 Introduction to cross-modal learning
• 86.2 Building cross-modal models with PyTorch
• 86.3 Learning joint representations from multiple modalities

Chapter 87: PyTorch for Zero-Shot Learning

• 87.1 Introduction to zero-shot learning
• 87.2 Building zero-shot learning models with PyTorch
• 87.3 Generalizing to unseen classes without labeled data

Chapter 88: PyTorch for Multimodal Learning

• 88.1 Introduction to multimodal learning
• 88.2 Building multimodal models with PyTorch
• 88.3 Fusion techniques for combining different modalities

Chapter 89: PyTorch for Time Series Forecasting with Transformers and Attention

• 89.1 Attention mechanisms in transformer-based time series forecasting
• 89.2 Building transformer models with attention for time series forecasting
• 89.3 Enhancing performance with attention mechanisms

Chapter 90: PyTorch for Few-Shot Semantic Segmentation

• 90.1 Introduction to few-shot semantic segmentation
• 90.2 Building few-shot semantic segmentation models with PyTorch
• 90.3 Adapting to new semantic segmentation tasks with limited labeled data

Chapter 91: PyTorch for Active Learning in Computer Vision

• 91.1 Introduction to active learning in computer vision
• 91.2 Building active learning workflows for computer vision tasks with PyTorch
• 91.3 Selecting informative samples for annotation

Chapter 92: PyTorch for Semi-Supervised Learning in Natural Language Processing

• 92.1 Introduction to semi-supervised learning in NLP
• 92.2 Building semi-supervised learning models with PyTorch
• 92.3 Utilizing unlabeled data for improved performance in NLP tasks

Chapter 93: PyTorch for Explainable Deep Learning in Computer Vision

• 93.1 Interpretability techniques for deep learning in computer vision
• 93.2 Building interpretable models with PyTorch
• 93.3 Visualizing and explaining model predictions

Chapter 94: PyTorch for Dynamic Graph Neural Networks

• 94.1 Introduction to dynamic graph neural networks
• 94.2 Building dynamic graph models with PyTorch
• 94.3 Handling temporal and evolving graph structures

Chapter 95: PyTorch for Collaborative Filtering

• 95.1 Introduction to collaborative filtering
• 95.2 Building collaborative filtering models with PyTorch
• 95.3 Recommender systems and personalized recommendations

Chapter 96: PyTorch for Time Series Forecasting with Convolutional Neural Networks

• 96.1 Convolutional neural networks for time series forecasting
• 96.2 Building CNN models for time series forecasting with PyTorch
• 96.3 Capturing temporal patterns with convolutional filters

Chapter 97: PyTorch for Weakly Supervised Object Localization

• 97.1 Introduction to weakly supervised object localization
• 97.2 Building weakly supervised object localization models with PyTorch
• 97.3 Localizing objects with limited or no bounding box annotations

Chapter 98: PyTorch for Natural Language Processing with Transformers

• 98.1 Introduction to transformers in NLP
• 98.2 Building transformer models for NLP tasks with PyTorch
• 98.3 Transfer learning and fine-tuning of transformer models

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Chapter 99: PyTorch for Few-Shot Learning with Meta-Learning

• 99.1 Introduction to few-shot learning with meta-learning
• 99.2 Building meta-learning models for few-shot learning with PyTorch
• 99.3 Adapting to new tasks with limited labeled data

Chapter 100: PyTorch for Time Series Anomaly Detection with Variational Autoencoders

• 100.1 Variational autoencoders for time series anomaly detection
• 100.2 Building variational autoencoder models for anomaly detection with PyTorch
• 100.3 Learning latent representations for anomaly detection in time series

章节索引：¶

第1章：PyTorch 入门

1.1 什么是PyTorch？

1.2 PyTorch相对其他库的优势

1.3 PyTorch的安装

1.4 PyTorch张量

第2章：PyTorch基础操作

2.1 张量的创建与操作

2.2 张量运算与函数

2.3 基于autograd的自动微分

第3章：PyTorch数据集与数据加载器

3.1 PyTorch中的数据集处理

3.2 自定义数据集构建

3.3 数据加载器的使用

第4章：PyTorch神经网络构建

4.1 神经网络架构设计

4.2 前向传播与反向传播定义

4.3 神经网络训练

第5章：损失函数与优化算法

5.1 PyTorch常用损失函数

5.2 优化器与参数更新

5.3 学习率调度策略

第6章：模型训练与评估

6.1 数据划分为训练集与测试集

6.2 训练循环与批量处理

6.3 模型性能评估

第7章：PyTorch迁移学习

7.1 迁移学习原理

7.2 PyTorch预训练模型

7.3 微调与特征提取技术

第8章：卷积神经网络（CNNs）

8.1 CNN基础概述

8.2 PyTorch中CNN架构搭建

8.3 CNN的训练与评估

第9章：循环神经网络（RNNs）

8.1 RNN基础概述

8.2 PyTorch中RNN架构搭建

8.3 RNN的训练与评估

第10章：生成对抗网络（GANs）

10.1 GAN基础原理

10.2 PyTorch中GAN架构搭建

10.3 GAN训练与样本生成

第11章：PyTorch强化学习

11.1 强化学习基础

11.2 PyTorch构建RL智能体

11.3 RL智能体训练与评估

第12章：PyTorch模型部署

12.1 PyTorch模型保存与加载

12.2 生产环境中的模型部署

12.3 模型优化与量化技术

第13章：PyTorch高级技术

13.1 自定义损失函数与指标

13.2 模型解释与可视化

13.3 不平衡数据集处理

第14章：PyTorch GPU计算

14.1 PyTorch的GPU加速应用

14.2 数据并行与分布式训练

第15章：PyTorch与ONNX

15.1 ONNX技术简介

15.2 PyTorch模型转换为ONNX格式

15.3 ONNX模型跨框架部署

第16章：PyTorch与TensorBoard

16.1 使用TensorBoard可视化训练过程

16.2 指标记录与网络图可视化

第17章：PyTorch与深度学习库集成（续）

17.1 PyTorch与TensorFlow集成

17.2 PyTorch与Keras互操作性

17.3 PyTorch与scikit-learn集成

第18章：模型可解释性技术

18.1 使用Captum解释PyTorch模型

18.2 特征重要性与归因分析方法

18.3 模型预测结果解释

第19章：PyTorch时间序列处理

19.1 时间序列数据预处理

19.2 时间序列预测的循环模型构建

19.3 时间序列模型评估与优化

第20章：PyTorch自然语言处理

20.1 文本预处理与分词技术

20.2 文本分类模型构建

20.3 机器翻译的序列到序列模型

第21章：自动编码器与变分自动编码器

21.1 自动编码器原理

21.2 PyTorch中自动编码器架构搭建

21.3 生成建模的变分自动编码器

第22章：机器人领域的PyTorch强化学习

22.1 强化学习在机器人中的应用

22.2 机器人任务的RL智能体构建

22.3 仿真环境与真实场景部署

第23章：PyTorch与贝叶斯深度学习

23.1 贝叶斯深度学习基础

23.2 PyTorch构建贝叶斯神经网络

23.3 不确定性估计与模型校准

第24章：深度学习时间序列分析

24.1 深度学习模型的时间序列预测

24.2 长短期记忆（LSTM）网络

24.3 时间卷积网络（TCN）

第25章：PyTorch与图神经网络

25.1 图神经网络（GNNs）基础

25.2 PyTorch中GNN架构搭建

25.3 节点分类与图表示学习

第26章：PyTorch联邦学习

26.1 联邦学习基础原理

26.2 PyTorch实现联邦学习

26.3 隐私保护与安全考量

第27章：PyTorch深度强化学习

27.1 深度Q网络（DQN）与值函数方法

27.2 策略梯度与演员-评论家算法

27.3 强化学习与深度学习结合技术

第28章：PyTorch计算机视觉应用

28.1 PyTorch目标检测

28.2 卷积网络的图像分割

28.3 人脸识别与情感检测

第29章：Transformer时间序列预测

29.1 Transformer模型基础

29.2 Transformer在时间序列预测中的应用

29.3 Transformer序列到序列模型

第30章：PyTorch自然语言生成

30.1 循环与Transformer模型的语言建模

30.2 PyTorch文本生成与聊天机器人

30.3 Transformer神经机器翻译

第31章：深度学习对抗攻击与防御（续）

31.1 PyTorch生成对抗样本

31.2 对抗攻击防御方法

第32章：PyTorch模型压缩与量化

32.1 模型压缩技术概述

32.2 模型量化与尺寸缩减

32.3 PyTorch模型剪枝与稀疏化

第33章：PyTorch与AutoML

33.1 AutoML技术简介

33.2 自动化神经架构搜索

33.3 PyTorch超参数优化

第34章：PyTorch时间序列异常检测

34.1 时间序列数据异常检测技术

34.2 PyTorch构建异常检测模型

34.3 异常检测结果评估与解释

第35章：PyTorch推荐系统

35.1 推荐系统基础原理

35.2 PyTorch协同过滤算法

35.3 神经网络推荐系统构建

第36章：PyTorch高级优化方法

36.1 随机梯度下降变体算法

36.2 自适应优化算法

36.3 二阶优化方法

第37章：PyTorch与知识图谱

37.1 知识图谱基础概述

37.2 PyTorch知识图谱表示与嵌入

37.3 图推理与链接预测技术

第38章：PyTorch可解释AI

38.1 LIME与SHAP解释黑盒模型

38.2 PyTorch可解释模型构建

38.3 可解释AI实际应用案例

第39章：PyTorch时间序列分类

39.1 时间序列分类基础

39.2 特征提取与表示学习

39.3 时间序列分类器构建

第40章：PyTorch与贝叶斯优化

40.1 贝叶斯优化基础

40.2 PyTorch实现贝叶斯优化

40.3 超参数调优与优化策略

第41章：PyTorch音频处理

41.1 音频数据预处理与特征提取

41.2 音频分类模型构建

41.3 PyTorch语音识别与合成

第42章：机器人领域的PyTorch强化学习

42.1 PyTorch与机器人框架集成

42.2 机器人控制的强化学习算法

42.3 仿真到真实场景的迁移学习

第43章：PyTorch时间序列聚类

43.1 时间序列聚类基础

43.2 特征提取与表示学习

43.3 时间序列数据聚类算法

第44章：PyTorch自然语言理解

44.1 文本分类与情感分析

44.2 命名实体识别与信息抽取

44.3 自然语言理解应用场景

第45章：PyTorch图像字幕生成

45.1 图像字幕生成技术概述

45.2 PyTorch图像字幕模型构建

45.3 图像字幕生成方法

第46章：图表示学习（续）

46.1 PyTorch图嵌入技术

46.2 图表示学习的图神经网络

46.3 GNN链接预测与节点分类

第47章：PyTorch目标检测

47.1 目标检测技术基础

47.2 PyTorch目标检测模型构建

47.3 目标检测训练与评估

第48章：PyTorch时间序列分割

48.1 时间序列分割基础

48.2 时间序列数据分割方法

48.3 PyTorch分割模型构建

第49章：PyTorch元学习

49.1 元学习基础原理

49.2 PyTorch元学习模型构建

49.3 小样本学习与自适应技术

第50章：PyTorch异常检测

50.1 异常检测技术概述

50.2 PyTorch异常检测模型构建

50.3 离群点检测与新颖性检测

第51章：PyTorch多任务学习

51.1 多任务学习基础

51.2 PyTorch多任务模型构建

51.3 联合训练与任务特定输出

第52章：PyTorch图生成

52.1 PyTorch图生成技术

52.2 图自动编码器与变分图模型

52.3 特定属性图生成方法

第53章：注意力机制时间序列预测

53.1 时间序列预测中的注意力机制

53.2 PyTorch注意力模型构建

53.3 注意力权重解释方法

第54章：PyTorch视频处理

54.1 视频数据预处理与变换

54.2 PyTorch视频分类模型构建

54.3 动作识别与视频生成技术

第55章：PyTorch半监督学习

55.1 半监督学习基础原理

55.2 PyTorch半监督模型构建

55.3 利用未标注数据提升性能

第56章：PyTorch分布外检测

56.1 分布外检测技术概述

56.2 PyTorch OOD检测模型构建

56.3 OOD检测评估与基准测试

第57章：PyTorch模型集成技术

57.1 深度学习集成方法概述

57.2 PyTorch模型集成构建

57.3 多模型结合提升性能

第58章：PyTorch可解释推荐系统

58.1 推荐系统可解释模型

58.2 PyTorch可解释推荐方法

58.3 用户建模与个性化推荐

第59章：PyTorch时间序列插补

59.1 时间序列缺失值插补技术

59.2 PyTorch插补模型构建

59.3 时间序列缺失值处理方法

第60章：持续学习（续）

60.1 持续学习基础原理

60.2 PyTorch持续学习模型构建

60.3 灾难性遗忘与正则化技术

第61章：PyTorch图像风格迁移

61.1 图像风格迁移技术概述

61.2 PyTorch风格迁移模型构建

61.3 图像间风格迁移方法

第62章：PyTorch与超参数优化库

62.1 超参数优化技术简介

62.2 PyTorch与超参数优化库集成

62.3 高效超参数搜索策略

第63章：PyTorch音乐生成

63.1 音乐数据预处理与表示

63.2 PyTorch音乐生成模型构建

63.3 原创音乐作品生成技术

第64章：医疗领域的PyTorch联邦学习

64.1 联邦学习在医疗中的应用

64.2 PyTorch隐私保护模型构建

64.3 分布式医疗数据协同学习

第65章：PyTorch图像超分辨率

65.1 图像超分辨率技术概述

65.2 PyTorch超分辨率模型构建

65.3 图像上采样与细节增强

第66章：PyTorch自监督学习

66.1 自监督学习基础原理

66.2 前置任务与自监督模型

66.3 表示迁移到下游任务技术

第67章：PyTorch StyleGAN与图像合成

67.1 StyleGAN与图像合成技术概述

67.2 PyTorch StyleGAN模型构建

67.3 高质量合成图像生成

第68章：PyTorch主动学习

68.1 主动学习技术基础

68.2 PyTorch主动学习工作流构建

68.3 查询策略与不确定性采样

第69章：Transformer时间序列异常检测

69.1 Transformer异常检测技术概述

69.2 PyTorch Transformer模型构建

69.3 时间序列异常检测方法

第70章：PyTorch持续强化学习

70.1 持续强化学习设置与挑战

70.2 PyTorch持续RL智能体构建

70.3 持续RL中探索与利用平衡

第71章：PyTorch三维目标检测

71.1 三维目标检测技术基础

71.2 PyTorch三维检测模型构建

71.3 三维数据集训练与评估

第72章：PyTorch主动视觉

72.1 主动视觉技术基础

72.2 PyTorch主动视觉模型构建

72.3 主动感知与信息增益策略

第73章：PyTorch小样本目标检测

73.1 小样本目标检测技术概述

73.2 PyTorch小样本检测模型构建

73.3 未见类别适应与泛化技术

第74章：表格数据分析（续）

74.1 PyTorch表格数据建模预处理

74.2 表格数据深度学习模型构建

74.3 特征工程与特征选择方法

第75章：PyTorch弱监督学习

75.1 弱监督学习技术基础

75.2 PyTorch弱监督模型构建

75.3 标签噪声与不完整标注学习

第76章：PyTorch文本风格迁移

76.1 文本风格迁移技术概述

76.2 PyTorch文本风格迁移模型构建

76.3 文本风格迁移方法

第77章：Transformer图表示学习

77.1 图Transformer网络基础

77.2 PyTorch图Transformer模型构建

77.3 图分类与图生成技术

第78章：PyTorch视频超分辨率

78.1 视频超分辨率技术概述

78.2 PyTorch视频超分辨率模型构建

78.3 视频质量与细节提升方法

第79章：自然语言处理的持续学习

79.1 NLP任务持续学习设置

79.2 PyTorch NLP持续学习模型构建

79.3 新任务适应与知识保留技术

第80章：PyTorch合成数据生成

80.1 PyTorch GAN合成数据生成

80.2 合成数据生成应用场景

80.3 合成数据评估与利用技术

第81章：PyTorch小样本自然语言理解

81.1 小样本NLU技术概述

81.2 PyTorch小样本NLU模型构建

81.3 有限标注示例学习方法

第82章：PyTorch知识蒸馏

82.1 知识蒸馏技术基础

82.2 大模型到小模型的知识蒸馏

82.3 模型压缩与性能权衡策略

第83章：PyTorch GAN反演

83.1 PyTorch GAN反演技术概述

83.2 生成样本潜在表示重建

83.3 图像编辑与风格迁移应用

第84章：PyTorch语音增强与分离

84.1 语音增强与分离技术基础

84.2 PyTorch语音增强模型构建

84.3 混合音频声源分离方法

第85章：PyTorch元迁移学习

85.1 小样本学习的元迁移学习

85.2 PyTorch元迁移学习模型构建

85.3 相关任务间知识迁移技术

第 86 章：PyTorch 跨模态学习

86.1 跨模态学习概述

86.2 PyTorch 跨模态模型构建

86.3 多模态联合表示学习

第 87 章：PyTorch 零样本学习

87.1 零样本学习基础原理

87.2 PyTorch 零样本学习模型构建

87.3 未见类别无标注数据泛化技术

第 88 章：PyTorch 多模态学习

88.1 多模态学习概述

88.2 PyTorch 多模态模型构建

88.3 多模态融合技术

第 89 章：注意力机制 Transformer 时间序列预测

89.1 Transformer 时间序列预测中的注意力机制

89.2 PyTorch 注意力 Transformer 模型构建

89.3 注意力机制性能提升方法

第 90 章：PyTorch 小样本语义分割

90.1 小样本语义分割技术概述

90.2 PyTorch 小样本语义分割模型构建

90.3 有限标注数据语义分割任务适应

第 91 章：计算机视觉中的 PyTorch 主动学习

91.1 计算机视觉主动学习概述

91.2 PyTorch 计算机视觉主动学习工作流构建

91.3 标注样本选择策略

第 92 章：自然语言处理中的 PyTorch 半监督学习

92.1 NLP 半监督学习概述

92.2 PyTorch NLP 半监督模型构建

92.3 未标注数据在 NLP 中的应用

第 93 章：计算机视觉中的可解释深度学习

93.1 计算机视觉模型解释技术

93.2 PyTorch 可解释模型构建

93.3 模型预测可视化与解释

第 94 章：PyTorch 动态图神经网络

94.1 动态图神经网络概述

94.2 PyTorch 动态图模型构建

94.3 时序图结构处理技术

第 95 章：PyTorch 协同过滤

95.1 协同过滤技术基础

95.2 PyTorch 协同过滤模型构建

95.3 推荐系统个性化推荐方法

第 96 章：卷积神经网络时间序列预测

96.1 时间序列预测的 CNN 应用

96.2 PyTorch CNN 时间序列预测模型构建

96.3 卷积核捕捉时序模式技术

第 97 章：弱监督目标定位

97.1 弱监督目标定位技术概述

97.2 PyTorch 弱监督目标定位模型构建

97.3 无边界框标注目标定位方法

第 98 章：Transformer 自然语言处理

98.1 NLP 中的 Transformer 技术

98.2 PyTorch Transformer NLP 模型构建

98.3 Transformer 迁移学习与微调

第 99 章：元学习小样本学习

99.1 元学习小样本学习概述

99.2 PyTorch 元学习小样本模型构建

99.3 少样本任务自适应技术

第 100 章：变分自动编码器时间序列异常检测

100.1 VAE 时间序列异常检测原理

100.2 PyTorch VAE 异常检测模型构建

100.3 时序数据潜在表示异常检测

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