Art and Science of Machine Learning 日本語版

Go to Course: https://www.coursera.org/learn/art-science-ml-jp

Introduction

### Course Review: Art and Science of Machine Learning 日本語版 **Overview** The "Art and Science of Machine Learning 日本語版" is an engaging course hosted on Coursera that takes a deep dive into the intricate world of machine learning (ML). Structured across six comprehensive modules, this course is designed to equip learners with the essential knowledge, sound judgment, and foundational testing skills necessary for optimizing and fine-tuning machine learning models to achieve peak performance. The course guides students through the intricacies of model adjustment and optimization, covering critical concepts such as regularization techniques and the effects of hyperparameters (like batch sizes and learning rates) on model performance. Additionally, learners will be introduced to commonly used optimization algorithms along with practical applications utilizing TensorFlow code. --- **Course Syllabus Breakdown** 1. **Overview** This introductory module welcomes students to the art and science of machine learning. It covers the fundamental skills needed for achieving optimal model performance, such as knowledge of machine learning principles, proper decision-making, and basics of testing. You'll learn about regularization strategies to generalize models and the impact of hyperparameters on performance. 2. **Machine Learning in Practice** Here, learners adjust batch sizes and learning rates to enhance model performance. The module provides practical insights into model optimization and how to apply these concepts using TensorFlow, ensuring that participants can translate theory into tangible results. 3. **Hyperparameter Tuning** Distinguishing between parameters and hyperparameters is crucial, and this module focuses on that differentiation. Participants delve into traditional grid search methods while also exploring smarter algorithms that transcend basic grid search approaches. The automation of hyperparameter tuning via Cloud ML Engine is an exciting topic that enhances the learning experience. 4. **The Theory of Machine Learning** This module covers both theory and application, emphasizing the creation of simple, concise models through regularization to achieve sparsity. Key concepts such as logistic regression are explained in detail, showcasing how they determine model performance. 5. **Theory of Neural Networks** Focusing specifically on neural networks, this module delves into the theoretical understandings necessary for mastering ML with these powerful tools. 6. **Embedding** Students will learn how to manage sparse data through embeddings, which significantly reduce memory consumption and accelerate training in ML models. This technique not only simplifies models but also enhances their generalization capabilities. 7. **Conclusion** The course wraps up with a synthesis of the key concepts learned throughout the modules, solidifying the learner's understanding and readiness to apply these insights in real-world scenarios. --- **Recommendation** I highly recommend the "Art and Science of Machine Learning 日本語版" for anyone seriously looking to delve into machine learning, especially those who prefer instruction in Japanese. Whether you're a novice hoping to solidify your foundational knowledge, or someone experienced in the field wishing to enhance your skills in model tuning and optimization, this course provides valuable insights and practical skills. The course structure is well-organized, the materials are comprehensive, and the hands-on experience with TensorFlow will greatly aid learners in understanding the application of theoretical concepts. Engaging with this course not only equips you with essential technical skills but also prepares you to face real-world challenges in machine learning projects. Overall, this course is a superb investment for personal and professional development in the rapidly advancing field of machine learning.

Syllabus

概要

「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、モデルのパフォーマンス改善に向けてバッチサイズと学習率を調整する方法、モデルを最適化する方法、TensorFlow コードにコンセプトを適用する方法を学習します。

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。

このモジュールでは、機械学習の理論と実践について説明します。まず、シンプルで簡潔なモデルを作成できるように、スパース性を実現する正則化の実行方法について説明します。次に、ロジスティック回帰について説明し、パフォーマンスが決まる仕組みを学習します。

このモジュールでは、特にニューラル ネットワークを使用した理論について詳しく説明します。

このモジュールでは、埋め込みを使用して、疎データを管理する方法と、疎データを使用する機械学習モデルのメモリ消費量を減らしてトレーニングを高速化する方法を学習します。埋め込みはディメンション数を削減する方法でもあり、その結果として、モデルをより単純で一般化できるようになります。