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.

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.

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.

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.)

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.

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.

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.

• 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.

1. Your certificates will carry over for any courses you’ve already completed.
2. If your subscription is currently active, you can access the updated labs and submit assignments without paying for the month again.
3. 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).
4. 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.
5. If you do not see the option to reset deadlines, contact Coursera via the Learner Help Center.

1. Your certificates will carry over for any courses you’ve already completed.
2. If your subscription is currently inactive, you will need to pay again to access the labs and submit assignments for those courses.

The Deep Learning Specialization is made up of 5 courses.

You can enroll in the Deep Learning Specialization on Coursera. You will watch videos and complete assignments on Coursera as well.

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.

A Coursera subscription costs $49 / month.

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

Yes! You can preview the course for free by accessing the entire first module at no cost. This allows you to explore the learning experience before deciding if you’d like to continue. If you want full access to all modules, assessments, and the certificate of completion, you’ll need to upgrade to the paid version.

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.

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.

You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you must re-purchase the course to obtain a certificate. If you audit the course for free, you will not receive a certificate.

If you complete all n courses in the S12n and are subscribed to the Specialization, you will also receive an additional certificate showing that you completed the entire 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.

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.

The Deep Learning Specialization are 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.