Deep Learning Specialization

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Deep Learning Specialization

Become a Machine Learning expert.

Master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques!

Taught in French (AI Dubbing)

•

Instructors: Andrew Ng

Top Instructor

1,000,257 already enrolled

5 course series

Get in-depth knowledge of a subject

4.8

from 147,246 reviews of courses in this program

Intermediate level

Recommended experience

Flexible schedule

3 months at 10 hours a week

Learn at your own pace

5 course series

Get in-depth knowledge of a subject

4.8

from 147,246 reviews of courses in this program

Intermediate level

Recommended experience

Flexible schedule

3 months at 10 hours a week

Learn at your own pace

What you'll learn

Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in French (AI Dubbing)

Build toward a degree

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Specialization - 5 course series

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.

Applied Learning Project

By the end you’ll be able to:

• 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 TensorFlow

• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning

• 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

• 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

Neural Networks and Deep Learning

Course 1, 25 hours

Course 1•25 hours

What you'll learn

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. 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. It 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.

Skills you'll gain

Category: Artificial Neural Networks

Artificial Neural Networks

Category: Deep Learning

Deep Learning

Category: Convolutional Neural Networks

Convolutional Neural Networks

Category: Artificial Intelligence and Machine Learning (AI/ML)

Artificial Intelligence and Machine Learning (AI/ML)

Category: Python Programming

Python Programming

Category: Model Optimization

Model Optimization

Category: Model Training

Model Training

Category: Artificial Intelligence

Artificial Intelligence

Category: Applied Machine Learning

Applied Machine Learning

Category: Supervised Learning

Supervised Learning

Category: Machine Learning Methods

Machine Learning Methods

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Course 2, 24 hours

Course 2•24 hours

What you'll learn

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. 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. It 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.

Skills you'll gain

Category: Model Optimization

Model Optimization

Category: Deep Learning

Deep Learning

Category: Model Training

Model Training

Category: Tensorflow

Tensorflow

Category: Performance Tuning

Performance Tuning

Category: Machine Learning Methods

Machine Learning Methods

Category: Applied Machine Learning

Applied Machine Learning

Category: Debugging

Debugging

Category: Artificial Neural Networks

Artificial Neural Networks

Category: Model Evaluation

Model Evaluation

Category: Artificial Intelligence and Machine Learning (AI/ML)

Artificial Intelligence and Machine Learning (AI/ML)

Category: Verification And Validation

Verification And Validation

Structuring Machine Learning Projects

Course 3, 7 hours

Course 3•7 hours

What you'll learn

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. 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. It 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.

Skills you'll gain

Category: Transfer Learning

Transfer Learning

Category: Model Training

Model Training

Category: Applied Machine Learning

Applied Machine Learning

Category: Decision Intelligence

Decision Intelligence

Category: Debugging

Debugging

Category: AI Workflows

AI Workflows

Category: Machine Learning Methods

Machine Learning Methods

Category: Artificial Intelligence and Machine Learning (AI/ML)

Artificial Intelligence and Machine Learning (AI/ML)

Category: Model Evaluation

Model Evaluation

Category: Machine Learning

Machine Learning

Category: Data-Driven Decision-Making

Data-Driven Decision-Making

Category: Deep Learning

Deep Learning

Category: Model Optimization

Model Optimization

Category: AI Product Strategy

AI Product Strategy

Convolutional Neural Networks

Course 4, 36 hours

Course 4•36 hours

What you'll learn

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.

By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data. 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. It 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.

Skills you'll gain

Category: Convolutional Neural Networks

Convolutional Neural Networks

Category: Transfer Learning

Transfer Learning

Category: Computer Vision

Computer Vision

Category: Deep Learning

Deep Learning

Category: Tensorflow

Tensorflow

Category: Embeddings

Embeddings

Category: Fine-tuning

Fine-tuning

Category: Applied Machine Learning

Applied Machine Learning

Category: Artificial Neural Networks

Artificial Neural Networks

Category: Data Preprocessing

Data Preprocessing

Category: Model Optimization

Model Optimization

Category: Network Architecture

Network Architecture

Category: Generative AI

Generative AI

Category: Image Analysis

Image Analysis

Sequence Models

Course 5, 37 hours

Course 5•37 hours

What you'll learn

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.

By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. 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. It 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.

Skills you'll gain

Category: Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs)

Category: Embeddings

Embeddings

Category: Natural Language Processing

Natural Language Processing

Category: Transfer Learning

Transfer Learning

Category: Hugging Face

Hugging Face

Category: Deep Learning

Deep Learning

Category: Large Language Modeling

Large Language Modeling

Category: Artificial Neural Networks

Artificial Neural Networks

Category: Generative AI

Generative AI

Category: Fine-tuning

Fine-tuning

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Build toward a degree

When you complete this Specialization, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹

This Specialization has ACE® recommendation. It is eligible for college credit at participating U.S. colleges and universities. Note: The decision to accept specific credit recommendations is up to each institution.

Learn more

Instructors

Top Instructor

Andrew Ng

DeepLearning.AI

52 Courses•9,953,579 learners

Top Instructor

Younes Bensouda Mourri

DeepLearning.AI

23 Courses•1,743,850 learners

Offered by

DeepLearning.AI

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Learner reviews across Deep Learning

4.8

avg. across 5 courses

5 stars
87.46%
4 stars
10.69%
3 stars
1.44%
2 stars
0.24%
1 star
0.16%

All courses

Course: Neural Networks and Deep Learning·5·

Reviewed on Jun 15, 2019

Thank you so for this wonderful course. Thank you Andrew Sir and the entire team. The forum especially is very lively and helpful. Thank you for making my learning experience exciting and brilliant.

Course: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization·4·

Reviewed on Jun 10, 2020

great and practical insight. carefully crafted assignments. still coding in python and the quirks coming with it are sometimes of equal difficulty if not worse than understanding the explained theory

Course: Structuring Machine Learning Projects·5·

Reviewed on May 10, 2020

Really a good course and got an insight into how to structure a machine learning project and some useful techniques for deep learning, such as transfer learning, multi-task, and end-to-end learning

View more reviews

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Frequently asked questions

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.

The Deep Learning Specialization is made up of 5 courses.

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.

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

Go to your Coursera account.
Click on My Purchases and find the relevant course or Specialization.
Click Email Receipt and wait up to 24 hours to receive the receipt.
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.

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:

Course 4 includes MobileNet (transfer learning) and U-Net (semantic segmentation).
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.

• 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.

• 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.

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.

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.

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.

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.

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.

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.

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.

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