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Laurence Moroney

ソフトウェア開発者であれば、拡張性のあるAI搭載アルゴリズムを構築したい場合、構築ツールの使い方を理解する必要があります。この講座は今後学んでいく「TensorFlow in Practice 専門講座」の一部であり、機械学習用の人気のオープンソースフレームワークであるTensorFlowのベストプラクティスを学習します。

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ソフトウェア開発者であれば、拡張性のあるAI搭載アルゴリズムを構築したい場合、構築ツールの使い方を理解する必要があります。この講座は今後学んでいく「TensorFlow in Practice 専門講座」の一部であり、機械学習用の人気のオープンソースフレームワークであるTensorFlowのベストプラクティスを学習します。

アンドリュー・エンの「 The Machine Learning(機械学習)」と「Deep Learning Specialization(ディープラーニング専門講座)」では、機械学習とディープラーニングの最も重要かつ基本的な原理を学習します。deeplearning.aiが提供する新しい「TensorFlow in Practice 専門講座」では、TensorFlowを使用してそれらの原理を実装し、拡張性のあるモデルを構築して現実世界の問題に適用する方法を学びます。ニューラルネットワークの仕組みについての理解を深めるには、「ディープラーニング専門講座」を受講することをお勧めします。

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What's inside

Syllabus

新しいプログラミングパラダイム 
TensorFlowの入門~上級者向け講座へようこそ。お会いできて嬉しいです。1週目では、機械学習とディープラーニングの概要に触れ、それらがどのようにして新しいプログラミングパラダイムを提供し、これまで未踏だったシナリオを開くための新しいツールセットを提供するのかを簡単にご紹介します。 必要なのは、基本的なプログラミングスキルだけで、あとは学習を進める中で習得できます。TensorFlow 1.xとTensorFlow 2.0アルファ版の両方で動作するコードを使って学んでいきます。まず、最初の動画で、アンドリューとローレンスの対話をご覧ください。これから学習するテーマについて話しています。
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コンピュータビジョンの紹介 
この講座の2週目へようこそ。1週目では、機械学習とディープラーニングがいかに新しいプログラミングパラダイムであるかを学びました。今週は次のレベルへ進んで、わずか数行のコードでコンピュータビジョンの問題を解きます。まずはローレンスとアンドリューの対話をご覧ください。二人がコンピュータビジョンへとご案内します。
畳み込みニューラルネットワークでビジョンを強化する 
3週目へようこそ。2週目では、コンピュータビジョンのための簡単なニューラルネットワークについて学びました。よく機能しましたが、少し単純すぎるアプローチでした。今週は、ここでローレンスとアンドリューが話しているように、それを発展させる方法を見ていきます。
現実世界の画像を使用する 
先週はディープニューラルネットワークの結果を畳み込みを使用して改善する方法を学習しました。順調な滑り出しにはなりましたが、使用したデータは非常に基本的なものでした。画像が大きい場合や、特徴が常に同じ場所にない場合は、どうなるでしょうか。アンドリューとローレンスが今週の学習内容、つまり、複雑な画像の取り扱いについて話しています。

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops skills using industry standard programming tools for TensorFlow
Uses both TensorFlow 1.x and 2.x for added flexibility for learners at different stages
Addresses the growing need for real-world applications in the field of machine learning
Taught by industry experts Laurence Moroney and Andrew Ng
Not a standalone course, this is part of a multipart series

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI、機械学習、ディープラーニングのための TensorFlow 入門 with these activities:
Review Tensorflow basics
Familiarizing yourself with Tensorflow basics will prepare you for the more advanced concepts covered in this course.
Browse courses on TensorFlow
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  • Revisit your notes or online resources on Tensorflow concepts.
  • Review code examples from the Tensorflow documentation.
  • Practice implementing simple Tensorflow models.
Complete guided tutorials on Computer Vision
Following guided tutorials will provide you with practical experience in Computer Vision, a key topic in this course.
Browse courses on Computer Vision
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  • Identify online tutorials or workshops on Computer Vision fundamentals.
  • Follow the tutorials step-by-step, implementing the concepts in Tensorflow.
  • Experiment with different parameters and datasets to enhance your understanding.
Mentor peers on TensorFlow concepts
Mentoring others will enhance your understanding of TensorFlow and reinforce your skills through teaching.
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  • Identify opportunities to assist classmates or colleagues who may need guidance with TensorFlow concepts.
  • Prepare and articulate explanations of TensorFlow topics clearly and effectively.
  • Provide constructive feedback and support to those you mentor.
Four other activities
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Show all seven activities
Solve practice problems on Convolutional Neural Networks
Solving practice problems will reinforce your understanding of Convolutional Neural Networks, a crucial component in this course.
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  • Find online platforms or textbooks that offer practice problems on CNNs.
  • Attempt to solve the problems independently, implementing the solutions in Tensorflow.
  • Review your solutions against provided answers or consult online forums for guidance.
Start a project to build a deep learning model
Initiating a project will provide you with hands-on experience in designing and implementing deep learning models.
Browse courses on Deep Learning Models
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  • Define the scope and objectives of your project.
  • Gather and prepare the necessary data for your model.
  • Choose an appropriate deep learning architecture and implement it using Tensorflow.
  • Train and evaluate your model, iterating to improve its performance.
  • Deploy your model and monitor its performance in a real-world setting.
Create a project using Tensorflow
Developing a project will allow you to apply your knowledge of Tensorflow and demonstrate your skills in a practical setting.
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  • Identify a problem or challenge that you can solve using Tensorflow.
  • Design a solution and implement it using Tensorflow, considering performance and efficiency.
  • Document your project, including the problem statement, approach, and results.
  • Share your project on platforms like GitHub or Kaggle to receive feedback and connect with the community.
Participate in a Kaggle competition using Tensorflow
Engaging in a Kaggle competition will challenge you to apply your Tensorflow skills in a competitive environment.
Browse courses on Kaggle Competitions
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  • Identify an appropriate Kaggle competition that aligns with your interests and skill level.
  • Study the competition guidelines and data provided.
  • Develop a solution using TensorFlow, optimizing for performance and accuracy.
  • Submit your solution and monitor your progress on the leaderboard.
  • Analyze the results and learn from the approaches of top-performing teams.

Career center

Learners who complete AI、機械学習、ディープラーニングのための TensorFlow 入門 will develop knowledge and skills that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineers design, develop, and deploy computer vision systems. They work with a variety of programming languages and software development tools to create systems that can see and understand the world around us. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. They work with a variety of programming languages and software development tools to create systems that can learn from data and make predictions. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Natural Language Processing Engineer
Natural Language Processing Engineers design, develop, and deploy natural language processing systems. They work with a variety of programming languages and software development tools to create systems that can understand and generate human language. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Deep Learning Engineer
Deep Learning Engineers design, develop, and deploy deep learning systems. They work with a variety of programming languages and software development tools to create systems that can learn from data and make predictions. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work in a variety of industries, including academia, industry, and government. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Speech Recognition Engineer
Speech Recognition Engineers design, develop, and deploy speech recognition systems. They work with a variety of programming languages and software development tools to create systems that can recognize and understand human speech. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Data Scientist
Data Scientists use their knowledge of machine learning, statistics, and data analysis to extract insights from data. They work with data from a variety of sources, including structured and unstructured data, to identify patterns and trends. This course may be useful in helping you build a foundation in machine learning, which is an essential skill for Data Scientists.
Software Engineer
Software Engineers design, develop, and maintain software systems. They work with a variety of programming languages and software development tools to create software that meets the needs of users. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. They work in a variety of industries, including finance, insurance, and healthcare. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Business Analyst
Business Analysts use their knowledge of business and technology to identify and solve business problems. They work with stakeholders from across the organization to gather requirements, analyze data, and make recommendations. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to create products that meet the needs of users. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for leading or participating in the design, development, deployment, and maintenance of machine learning systems. You will need to have a strong understanding of machine learning algorithms, as well as experience with programming languages and software development tools. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Data Architect
Data Architects design and manage data systems. They work with stakeholders from across the organization to gather requirements, analyze data, and make recommendations. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Database Administrator
Database Administrators are responsible for the installation, configuration, and maintenance of database systems. They work with stakeholders from across the organization to ensure that databases are available, reliable, and secure. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with stakeholders from across the organization to ensure that projects are completed on time, within budget, and to the required quality. This course may be useful in helping you develop the skills and knowledge you need to succeed in this role.

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AI、機械学習、ディープラーニングのための TensorFlow 入門.
この本はディープラーニングの基礎をカバーする重要なリソースです。理論的な側面と実践的な応用の両方を提供し、TensorFlowの理解を深めます。
この本はTensorFlowを使用して自然言語処理を実装する方法に関する包括的なガイドです。追加のリソースと実用的な例を提供し、TensorFlowのより深い理解につながります。
この本はTensorFlowを使用して時系列解析を実装する方法に関する包括的なガイドです。追加のリソースと実用的な例を提供し、TensorFlowのより深い理解につながります。
この本はTensorFlowを使用してディープラーニングを構築するための詳細なガイドです。追加のリソースと例を提供し、TensorFlowの理解を深めます。
この本はTensorFlowを使用して数値計算を行う方法に関する包括的なガイドです。追加のリソースと実用的な例を提供し、TensorFlowのより深い理解につながります。
この本は機械学習ライブラリであるScikit-Learn、Keras、TensorFlowの使用に関する包括的なガイドです。TensorFlow入門に役立ち、追加のリソースを提供します。
この本はPythonを使用して機械学習を実装するための包括的なガイドです。TensorFlowを補完し、追加のリソースと実用的な例を提供します。

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