Deep Learning Specialization

• What is the refund policy?

  If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

• Can I just enroll in a single course?

  Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

• Is financial aid available?

  Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

• Can I take the course for free?

  When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

• Is this course really 100% online? Do I need to attend any classes in person?

  This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

• Will I earn university credit for completing the Specialization?

  This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.

• 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 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.

More questions? Visit the Learner Help Center.