About this 専門講座
100%オンラインコース

100%オンラインコース

自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール

フレキシブルなスケジュール

柔軟性のある期限の設定および維持
中級レベル

中級レベル

修了時間

約3か月で修了

推奨10時間/週
利用可能な言語

英語

字幕:英語, 中国語(繁体), ウクライナ語, 中国語(簡体), ポルトガル語(ブラジル), 韓国語, トルコ語, 日本語...

習得するスキル

TensorflowConvolutional Neural NetworkArtificial Neural NetworkDeep Learning
100%オンラインコース

100%オンラインコース

自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール

フレキシブルなスケジュール

柔軟性のある期限の設定および維持
中級レベル

中級レベル

修了時間

約3か月で修了

推奨10時間/週
利用可能な言語

英語

字幕:英語, 中国語(繁体), ウクライナ語, 中国語(簡体), ポルトガル語(ブラジル), 韓国語, トルコ語, 日本語...

How the 専門講座 Works

コースを受講しましょう。

Coursera(コーセラ)の専門講座は、一連のコース群であり、技術を身に付ける手助けとなります。開始するには、専門講座に直接登録するか、コースを確認して受講したいコースを選択してください。専門講座の一部であるコースにサブスクライブすると、自動的にすべての専門講座にサブスクライブされます。1つのコースを修了するだけでも結構です。いつでも、学習を一時停止したり、サブスクリプションを終了することができます。コースの登録状況や進捗を追跡するには、受講生のダッシュボードにアクセスしてください。

実践型プロジェクト

すべての専門講座には、実践型プロジェクトが含まれています。専門講座を完了して修了証を獲得するには、成功裏にプロジェクトを終了させる必要があります。専門講座に実践型プロジェクトに関する別のコースが含まれている場合、専門講座を開始するには、それら他のコースをそれぞれ終了させる必要があります。

修了証を取得

すべてのコースを終了し、実践型プロジェクトを完了すると、修了証を獲得します。この修了証は、今後採用企業やあなたの職業ネットワークと共有できます。

how it works

この専門講座には5コースあります。

コース1

Neural Networks and Deep Learning

4.9
41,271件の評価
8,301件のレビュー
If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization....
コース2

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

4.9
26,169件の評価
2,944件のレビュー
This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization....
コース3

Structuring Machine Learning Projects

4.8
21,431件の評価
2,442件のレビュー
You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....
コース4

Convolutional Neural Networks

4.8
16,174件の評価
2,128件のレビュー
This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization....

講師

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Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
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Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
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Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai

業界パートナー

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deeplearning.aiについて

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

よくある質問

  • はい。まず始めに興味のあるコースカードをクリックして登録します。コースに登録して修了することによって、共有できる修了証を取得するか、無料でコースを聴講してコースの教材を確認することができます。専門講座の一部であるコースにサブスクライブすると、専門講座全体に自動的にサブスクライブされます。進捗を追跡するには、受講生のダッシュボードにアクセスしてください。

  • このコースは完全にオンラインで提供されているため、実際に教室に出席する必要はありません。Webまたはモバイル機器からいつでもどこからでも講義、学習用教材、課題にアクセスできます。

  • この専門講座では大学の単位は付与されませんが、一部の大学では専門講座修了証を単位として承認する場合があります。詳細については、大学にお問い合わせください。

  • Expected:

    Programming experience. The course is taught in Python. We assume you have basic programming skills (understanding of for loops, if/else statements, data structures such as lists and dictionaries).

    Recommended:

    - Mathematics: basic linear algebra (matrix vector operations and notation) will help.

    - Machine Learning: a basic knowledge of machine learning (how do we represent data, what does a machine learning model do) will help. If you have taken Andrew Ng's Machine Learning course on Coursera, you're good of course!

  • No, these courses have sessions that start every few weeks. Once you enroll in a Specialization, you can take the courses at your own pace and even switch sessions if you fall behind. Please visit the Learner Help Center if you have any more questions about enrollment and sessions: https://learner.coursera.help/hc/en-us/articles/209818613

  • To request a receipt: In your Coursera account, open your My Purchases page. Find the course or Specialization you want a receipt for, and click "Email Receipt." The receipt will be sent within 24 hours. More instructions on requesting a receipt are here: https://learner.coursera.help/hc/en-us/articles/208280236

  • Please go to https://www.coursera.org/enterprise for more information, to contact Coursera, and to pick a plan. For each plan, you decide the number of courses each person can take and hand-pick the collection of courses they can choose from.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。