深度学习 專項課程

• 退款政策是如何规定的？

  如果订阅，您可以获得 7 天免费试听，在此期间，您可以取消课程，无需支付任何罚金。在此之后，我们不会退款，但您可以随时取消订阅。请阅读我们完整的退款政策。

• 我可以只注册一门课程吗？

  可以！点击您感兴趣的课程卡开始注册即可。注册并完成课程后，您可以获得可共享的证书，或者您也可以旁听该课程免费查看课程资料。如果您订阅的课程是某专项课程的一部分，系统会自动为您订阅完整的专项课程。访问您的学生面板，跟踪您的进度。

• 有助学金吗？

  是的。如果您负担不起学习计划的注册费，则可以在选择学习计划时申请助学金或奖学金。如果您选择的学习计划提供助学金或奖学金，则您将在描述页面上看到一个申请链接。

• 我可以免费学习课程吗？

  完成注册课程后，您可以学习专项课程中的所有课程，并且完成作业后可以获得证书。如果您只想阅读和查看课程内容，可以免费旁听该课程。如果您无法承担课程费用，可以申请助学金。

• 此课程是 100% 在线学习吗？是否需要现场参加课程？

  此课程完全在线学习，无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

• 完成专项课程后我会获得大学学分吗？

  此专项课程不提供大学学分，但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校了解详情。

• What is Deep Learning? Why is it relevant?

  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 a 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 and train 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. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also 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 architecture parameters, and apply DL to your applications. 2. Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow 3. Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning 4. Build a Convolutional Neural Network, 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 Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition 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.

• The Deep Learning Specialization was updated in April 2021. What is different in the new version?

  • 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.

• Can I get college credit for taking the Deep Learning Specialization?

  Those planning to attend a degree program can utilize ACE®️ recommendations, the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 10 college credits for completing the Deep Learning Specialization. This aims to help open up additional pathways to learners who are interested in higher education, and prepare them for entry-level jobs.

  To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credly badge, which contains the ACE®️ credit recommendation.  Once claimed, they will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed. 

• How do I pursue the ACE credit recommendation?

  To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credly badge, which contains the ACE®️ credit recommendation.  Once claimed, they will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed.

• How do I know which colleges and universities grant credit for the Deep Learning Specialization?

  The Deep Learning Specialization is eligible for college credit at participating colleges and universities nationwide. The decision to accept specific credit recommendations is up to each institution and not guaranteed. Read more about  ​ACE Credit College & University Partnerships here.

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