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PyTorchによる中級ディープラーニング

中級スキルレベル

更新日 2025/06

画像データや時系列データのモデリングに用いられる、CNN（畳み込みニューラルネットワーク）、RNN（再帰型ニューラルネットワーク）、LSTM（長短期記憶ネットワーク）、GRU（ゲート再帰ユニット）といった基本的な深層学習アーキテクチャについて学びましょう。

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PyTorchArtificial Intelligence

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コース説明

前提条件

Introduction to Deep Learning with PyTorch

1

Training Robust Neural Networks

Learn how to train neural networks in a robust way. In this chapter, you will use object-oriented programming to define PyTorch datasets and models and refresh your knowledge of training and evaluating neural networks. You will also get familiar with different optimizers and, finally, get to grips with various techniques that help mitigate the problems of unstable gradients so ubiquitous in neural nets training.

PyTorch and object-oriented programming

PyTorch Dataset

PyTorch DataLoader

PyTorch Model

Optimizers, training, and evaluation

Training loop

Model evaluation

Vanishing and exploding gradients

Initialization and activation

Activations: ReLU vs. ELU

Batch Normalization

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2

Images & Convolutional Neural Networks

Train neural networks to solve image classification tasks. In this chapter, you will learn how to handle image data in PyTorch and get to grips with convolutional neural networks (CNNs). You will practice training and evaluating an image classifier while learning about how to improve the model performance with data augmentation.

Handling images with PyTorch

Image dataset

Data augmentation

Data augmentation in PyTorch

Convolutional Neural Networks

The convolutional layer

Building convolutional networks

Training image classifiers

Choosing augmentations

Dataset with augmentations

Image classifier training loop

Evaluating image classifiers

Multi-class model evaluation

Analyzing metrics per class

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3

Sequences & Recurrent Neural Networks

Build and train recurrent neural networks (RNNs) for processing sequential data such as time series, text, or audio. You will learn about the two most popular recurrent architectures, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as well as how to prepare sequential data for model training. You will practice your skills by training and evaluating a recurrent model for predicting electricity consumption.

Handling sequences with PyTorch

Generating sequences

Sequential Dataset

Recurrent Neural Networks

Sequential architectures

Building a forecasting RNN

LSTM and GRU cells

RNN vs. LSTM vs. GRU

LSTM network

GRU network

Training and evaluating RNNs

RNN training loop

Evaluating forecasting models

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4

Multi-Input & Multi-Output Architectures

Build multi-input and multi-output models, demonstrating how they can handle tasks requiring more than one input or generating multiple outputs. You will explore how to design and train these models using PyTorch and delve into the crucial topic of loss weighting in multi-output models. This involves understanding how to balance the importance of different tasks when training a model to perform multiple tasks simultaneously.

Multi-input models

Two-input dataset

Two-input model

Training two-input model

Multi-output models

Two-output Dataset and DataLoader

Two-output model architecture

Training multi-output models

Evaluation of multi-output models and loss weighting

Multi-output model evaluation

Loss weighting

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PyTorchによる中級ディープラーニング

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