ディープラーニング専門講座

• 返金ポリシーについて教えてください。

  サブスクライブすると、7日間の無料トライアルを体験できます。この期間中は解約金なしでキャンセルできます。それ以降、払い戻しはありませんが、サブスクリプションをいつでもキャンセルできます。返金ポリシーをすべて表示します。

• 1つのコースだけに登録することは可能ですか？

  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。

• 学資援助はありますか？

  はい。受講料の支払いが難しい受講生に、Coursera（コーセラ）は学資援助を提供しています。左側の［登録］ボタンの下にある［学資援助］のリンクをクリックして申請してください。申請書の入力を促すメッセージが表示され、承認されると通知が届きます。キャップストーンプロジェクトを含む専門講座の各コースでこのステップを完了する必要があります。詳細。

• 無料でコースを受講できますか？

  コースに登録すると専門講座のすべてのコースにアクセスできるようになり、コースを修了すると修了証を取得できます。コース内容の閲覧のみを希望する場合は、無料でコースを聴講することができます。受講料の支払いが難しい場合は、学資援助を申請することができます。

• このコースは100%オンラインで提供されますか？実際に出席する必要のあるクラスはありますか？

  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。

• 専門講座を修了することで大学の単位は付与されますか？

  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。

• 専門講座を修了することで大学の単位は付与されますか？

  Deep Learning is a subset of machine learning where artificial neural networks, algorithms based on the structure and functioning of the human brain, learn from large amounts of data to create patterns for decision-making. Neural networks with various (deep) layers enable learning through performing tasks repeatedly and tweaking them a little to improve the outcome. 

  Over the last few years, the availability of computing power and the amount of data being generated have led to an increase in deep learning capabilities. Today, deep learning engineers are highly sought after, and deep learning has become one of the most in-demand technical skills as it provides you with the toolbox to build robust AI systems that just weren’t possible a few years ago. Mastering deep learning opens up numerous career opportunities.

• What is the Deep Learning Specialization about?

  The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. 

   In this Specialization, you will build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. You will master these theoretical concepts and their industry applications using Python and TensorFlow. You will tackle real-world case studies such as autonomous driving, sign language reading, music generation, computer vision, speech recognition, and natural language processing. 

   AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. Along the way, you will get career advice from deep learning experts from industry and academia.

• What will I be able to do after completing the Deep Learning Specialization?

  By the end of the Deep Learning Specialization, you will be able to:

  1. Build and train deep neural networks, implement vectorized neural networks, identify key parameters in architecture, and apply deep learning to your applications 2. Use the best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard neural network techniques, apply optimization algorithms, and implement a neural network in TensorFlow 3. Diagnose and use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end learning, transfer learning, and multi-task learning 4. Build a CNN, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data 5. Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering

• What background knowledge is necessary for the Deep Learning Specialization?

  Expected: 

  • Learners should have intermediate Python experience (e.g., basic programming skills, understanding of for loops, if/else statements, data structures such as lists and dictionaries).

  Recommended: 

  • Learners should have a basic knowledge of linear algebra (matrix-vector operations and notation).
  • Learners should have an understanding of machine learning concepts (how to represent data, what an ML model does, etc.)

• Who is the Deep Learning Specialization for?

  The Deep Learning Specialization is for early-career software engineers or technical professionals looking to master fundamental concepts and gain practical machine learning and deep learning skills.

• How long does it take to complete the Deep Learning Specialization?

  The Deep Learning Specialization consists of five courses. At the rate of 5 hours a week, it typically takes 5 weeks to complete each course except course 3, which takes about 4 weeks.

• Who is the Deep Learning Specialization by?

  The Deep Learning Specialization has been created by Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mourri. 

  Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world's largest MOOC platform. 

  Kian Katanforoosh is the co-founder and CEO of Workera and a lecturer in the Computer Science department at Stanford University. Workera allows data scientists, machine learning engineers, and software engineers to assess their skills against industry standards and receive a personalized learning path. Kian is also the recipient of Stanford’s Walter J. Gores award (Stanford’s highest teaching award) and the Centennial Award for Excellence in teaching.

  Younes Bensouda Mourri completed his Bachelor's in Applied Mathematics and Computer Science and Master's in Statistics from Stanford University. Younes helped create 3 AI courses at Stanford - Applied Machine Learning, Deep Learning, and Teaching AI - and taught two of them for a few years.

• Is this a standalone course or a Specialization?

  The Deep Learning Specialization is made up of 5 courses.

• Do I need to take the courses in a specific order?

  We recommend taking the courses in the prescribed order for a logical and thorough learning experience. Course 3 can also be taken as a standalone course.

• Can I apply for financial aid?

  Yes, Coursera provides financial aid to learners who cannot afford the fee.

• Can I audit the Deep Learning Specialization?

  You can audit the courses in the Deep Learning Specialization for free. 

  Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.

• How do I get a receipt to get this reimbursed by my employer?

  1. Go to your Coursera account. 
  2. Click on My Purchases and find the relevant course or Specialization.
  3. Click Email Receipt and wait up to 24 hours to receive the receipt. 
  4. You can read more about it here.

• I want to purchase this Specialization for my employees! How can I do that?

  Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.

• Why was the Deep Learning Specialization updated in April 2021?

  Changes in TensorFlow API: Since this Specialization was launched back in 2017, there have been changes to the TensorFlow API. With this refresh, learners can access updated lectures, quizzes, and assignments. Extended Syllabus: The updated program material includes leading-edge techniques and concepts that will better prepare learners to apply their deep learning skills for building real-world applications.

• What is different in the new version of the Deep Learning Specialization?

  • All existing assignments and autograders have been refactored and updated to TensorFlow 2 across Courses 1, 2, 4, and 5.
  • Three new network architectures are presented with new lectures and programming assignments:

  1. Course 4 includes MobileNet (transfer learning) and U-Net (semantic segmentation).
  2. Course 5, once updated, will include Transformers (Network Architecture, Named Entity Recognition, Question Answering).

  • For a detailed list of changes, please check out the DLS Changelog.

• I’m currently enrolled in one or more courses in the Deep Learning Specialization. What does this mean for me?

  • Your certificates will carry over for any courses you’ve already completed.

  • If your subscription is currently active, you can access the updated labs and submit assignments without paying for the month again.

  • If you go to the Specialization, you will see the original version of the lecture videos and assignments. You can complete the original version if so desired (this is not recommended).

  • If you would like to update to the new material, reset your deadlines. If you’re in the middle of a course, you will lose your notebook work when you reset your deadlines. Please save your work by downloading your existing notebooks before switching to the new version.

  • If you do not see the option to reset deadlines, contact Coursera via the Learner Help Center.

• I’ve already completed one or more courses in the Deep Learning Specialization but don’t have an active subscription. What does this mean for me?

  • Your certificates will carry over for any courses you’ve already completed.

  • If your subscription is currently inactive, you will need to pay again to access the labs and submit assignments for those courses.

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。