Question 1

What is Deep Learning? Why is it relevant?

Accepted Answer

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.

Question 2

What is the Deep Learning Specialization about?

Accepted Answer

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.

Question 3

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

Accepted Answer

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.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 TensorFlowUse strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learningBuild 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 dataBuild 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

Question 4

What background knowledge is necessary for the Deep Learning Specialization?

Accepted Answer

Question 5

Who is the Deep Learning Specialization for?

Accepted Answer

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.

Question 6

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

Accepted Answer

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.

Question 7

Who is the Deep Learning Specialization by?

Accepted Answer

Question 8

Is this a standalone course or a Specialization?

Accepted Answer

The Deep Learning Specialization is made up of 5 courses.

Question 9

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

Accepted Answer

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.

Question 10

Can I apply for financial aid?

Accepted Answer

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

Question 11

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

Accepted Answer

Question 12

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

Accepted Answer

Question 13

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

Accepted Answer

Question 14

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

Accepted Answer

Question 15

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?

Accepted Answer

• 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.

Question 16

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

Accepted Answer

Question 17

How do I pursue the ACE credit recommendation?

Accepted Answer

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.

Question 18

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

Accepted Answer

Question 19

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

Accepted Answer

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.

Question 20

Can I just enroll in a single course?

Accepted Answer

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. 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.

Question 21

Is financial aid available?

Accepted Answer

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Question 22

Can I take the course for free?

Accepted Answer

No, you cannot take this 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 cannot afford the fee, you can apply for financial aid.

Question 23

Will I earn university credit for completing the Specialization?

Accepted Answer

This Specialization doesn&#x27;t carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Deep Learning | Coursera

Deep Learning Specialization Overview

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