• Какие правила возврата средств?

  Когда вы оформите подписку, начнется семидневный бесплатный пробный период, в течение которого подписку можно отменить без штрафа. По истечении этого срока вы не сможете вернуть средства, но сможете отменить подписку в любой момент. Ознакомьтесь с нашей политикой возврата средств.

• Можно ли зарегистрироваться только на один курс?

  Да! Чтобы начать, нажмите карточку интересующего вас курса и зарегистрируйтесь. Зарегистрировавшись, вы можете пройти курс и получить сертификат, ссылкой на который можно делиться с другими людьми. Просто ознакомиться с содержанием курса можно бесплатно. При подписке на курс, входящий в специализацию, вы автоматически подписываетесь на всю специализацию. Ход учебы можно отслеживать в панели управления учащегося.

• Можно ли получить финансовую помощь?

  Да, Coursera предоставляет финансовую помощь учащимся, которые не могут оплатить обучение. Чтобы подать заявление, перейдите по ссылке "Финансовая помощь" слева под кнопкой "Зарегистрироваться". Заполните форму заявления. Если его примут, вы получите уведомление. Обратите внимание: этот шаг необходимо выполнить для каждого курса специализации, в том числе для дипломного проекта. Подробнее

• Могу ли я пройти курс бесплатно?

  Когда вы регистрируетесь на курс, то можете получить доступ ко всем курсам в специализации. Кроме того, вы получаете сертификат о прохождении курса.Просто ознакомиться с содержанием курса можно бесплатно. Если стоимость обучения для вас слишком велика, вы можете подать заявку на финансовую помощь.

• Действительно ли это полностью дистанционный курс? Нужно ли мне посещать какие-либо занятия лично?

  Это полностью дистанционный курс, потому вам не нужно ничего посещать. Все лекции, материалы для самостоятельного изучения и задания доступны всегда и везде по Интернету и с мобильных устройств.

• Получу ли я зачеты в университете за прохождение специализации?

  Эта специализация не приравнивается к зачету в университетах, однако некоторые вузы принимают сертификаты на свое усмотрение. Дополнительную информацию уточняйте в своем деканате.

• Получу ли я зачеты в университете за прохождение специализации?

  Эта специализация не приравнивается к зачету в университетах, однако некоторые вузы принимают сертификаты на свое усмотрение. Дополнительную информацию уточняйте в своем деканате. Онлайн-дипломы и сертификаты Mastertrack™ от Coursera позволяют получить зачеты.

• 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.

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