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87943410
Python機械学習プログラミング［PyTorch＆scikit-learn編］ impress top gear
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- 第3版まで続くロングセラーのPyTorch版！機械学習の基本から先進的な手法まで本格解説手法の背景にある理論・数式も説明『機械学習を実践的に学ぶための優れたテキスト』『多くのトピックを網羅した深い一冊。強力にお勧め』―原著への読者の声本書の前半は、基本的な機械学習ライブラリのscikit-learnを使った手法を解説。分類の基本モデルに始まり、単層ニューラルネットまでを実装するほか、データ前処理、次元削減、ハイパーパラメーターのチューニング、アンサンブル学習、回帰分析などを取り上げます。後半では、PyTorchによるさまざまなディープラーニングの手法を説明。PyTorchの仕組みを示したあと、CNN／RNN／Transformerといったモデルの実装を解説。敵対的生成ネットワーク、グラフニューラルネットワーク、強化学習もカバー。【本書に寄せて（一部抜粋）】この新しいPyTorch版では、Transformerアーキテクチャとグラフニューラルネットワークに関する新しい章が追加されている。これらはディープラーニングの最先端のアプローチであり、この2年間にテキスト理解と分子構造の分野を席巻している。Hugging Face、PyTorch Lightning、PyTorch Geometricなど、このエコシステムにおいてすでに広く人気を獲得している新しいソフトウェアパッケージを使って、そうしたアプローチを試してみることができる。機械学習という意欲的な分野の概観としても、実践的な知見の宝庫としても、本書がかけがえのないものであることがわかると確信している。―Dmytro Dzhulgakov：PyTorch Core Maintainer【章構成】第1章 「データから学習する能力」をコンピュータに与える第2章 分類問題―単純な機械学習アルゴリズムの訓練第3章 分類問題―機械学習ライブラリscikit-learnの活用第4章 データ前処理―よりよい訓練データセットの構築第5章 次元削減でデータを圧縮する第6章 モデルの評価とハイパーパラメータのチューニングのベストプラクティス第7章 アンサンブル学習―異なるモデルの組み合わせ第8章 機械学習の適用―感情分析第9章 回帰分析―連続値をとる目的変数の予測第10章 クラスタ分析―ラベルなしデータの分析第11章 多層人工ニューラルネットワークを一から実装する第12章 ニューラルネットワークの訓練をPyTorchで並列化する第13章 PyTorchのメカニズム第14章 画像の分類―ディープ畳み込みニューラルネットワーク第15章 系列データのモデル化―リカレントニューラルネットワーク第16章 Transformer―Attentionメカニズムによる自然言語処理の改善第17章 新しいデータの合成―敵対的生成ネットワーク第18章 グラフニューラルネットワーク―グラフ構造データでの依存性の捕捉第19章 複雑な環境での意思決定―強化学習◎本書は『Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python』の翻訳書です。
| Publisher | インプレス |
| Publication date | December 15, 2022 |
| Language | Japanese |
| Print length | 712 pages |
| ISBN-10 | 429501558X |
| ISBN-13 | 978-4295015581 |
| Item Weight | 880 g |
| Dimensions | 9.13 x 7.17 x 1.18 inches (23.2 x 18.2 x 3 cm) |
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