「Art and Science of machine learning」へようこそ。このコースは 6 つのモジュールで構成されています。このコースでは、機械学習モデルの詳細な調整や最適化によって最高のパフォーマンスを実現するために必要な、機械学習の知識、適切な判断、テストの基本的なスキルについて説明します。正則化の手法を使用してモデルを一般化する方法と、ハイパーパラメータの影響について学習します（モデルのパフォーマンスに対するバッチサイズや学習率の影響など）。一般的なモデル最適化アルゴリズムをいくつか説明し、TensorFlow コードで最適化メソッドを指定する方法を示します。
This course is part of the Machine Learning with TensorFlow Google Cloud 日本語版 Specialization
Art and Science of Machine Learning 日本語版
About this Course
Skills you will gain
- Uncertainty Analysis
- Financial Analysis
- Motion Chart
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
「Art and Science of Machine Learning」へようこそ。このコースでは、機械学習モデルの詳細な調整や最適化によって最高のパフォーマンスを実現するために必要な、機械学習の知識、適切な判断、テストの基本的なスキルについて説明します。正則化の手法を使用してモデルを一般化する方法と、ハイパーパラメータの影響について学習します（モデルのパフォーマンスに対するバッチサイズや学習率の影響など）。一般的なモデル最適化アルゴリズムをいくつか説明し、TensorFlow コードで最適化メソッドを指定する方法を示します。
このモジュールでは、パラメータとハイパーパラメータを区別する方法を学習します。その後、従来のグリッド検索のアプローチについて確認し、よりスマートなアルゴリズムを使用することでグリッド検索の枠を超えて考える方法を学習します。最後に、Cloud ML Engine によってハイパーパラメータ調整の自動化が便利になる仕組みを学習します。
About the Machine Learning with TensorFlow Google Cloud 日本語版 Specialization
問題を設定し、勾配降下を使用して適切な解決策を見つけ、データセットを作成する方法について学びます。また、Tensorflow でスケーラブルな分散型機械学習モデルを作成して、これらのモデルのトレーニングを拡張し、高性能な予測ができるようになります。さらに、機械学習（ML）がデータから重要な特質を学習したり、人間による分析を問題に取り入れるように、生データを変換します。最後に、正確で一般化されたモデルを生成し、特定の ML 問題を解決する理論について、および適切なパラメータの組み合わせ方を学びます。まず ML 集中型の戦略の構築から始め、その後 Google Cloud Platform のハンズオンラボを通じてモデルのトレーニング、最適化、本稼働まで、ML に関する手順全体を実習します。
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Last Updated: September 1, 2015
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