「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。
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Machine Learning with TensorFlow Google Cloud 日本語版 특화 과정 정보
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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