We may earn an affiliate commission when you visit our partners.
Jong-Moon Chung
Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers. The second part focuses on the core technologies of DL and ML systems, which include NN (Neural Network), CNN (Convolutional NN), and RNN (Recurrent NN) systems. The third part focuses on four TensorFlow Playground projects, where experience on designing DL NNs can be gained using an easy and fun yet very powerful application called the TensorFlow Playground. This course was designed to help you build business strategies and enable you to conduct technical planning on new DL and ML services and products.
Register for this course and see more details by visiting:
OpenCourser.com/course/89wikk/deep
Two deals to help you save
Save money when you learn. Here are two deals and offers that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until July 14
Coursera Plus Annual
Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.
Up to
40%
off
Get offer
Valid until August 30
Google AI App Builder
Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind
Take
100%
off
Get offer
What's inside
Syllabus
Deep Learning Products & Services
For the course “Deep Learning for Business,” the first module is “Deep Learning Products & Services,” which starts with the lecture “Future Industry Evolution & Artificial Intelligence” that explains past, current, and future industry evolutions and how DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of future industry in the near future. The following lectures look into the hottest DL and ML products and services that are exciting the business world. First, the “Jeopardy!” winning versatile IBM Watson is introduced along with its DeepQA and AdaptWatson systems that use DL technology. Then the Amazon Echo and Echo Dot products are introduced along with the Alexa cloud based DL personal assistant that uses ASR (Automated Speech Recognition) and NLU (Natural Language Understanding) technology. The next lecture focuses on LettuceBot, which is a DL system that plants lettuce seeds with automatic fertilizer and herbicide nozzles control. Then the computer vision based DL blood cells analysis diagnostic system Athelas is introduced followed by the introduction of a classical and symphonic music composing DL system named AIVA (Artificial Intelligence Virtual Artist). As the last topic of module 1, the upcoming Apple watchOS 4 and the HomePod speaker that was presented at Apple's 2017 WWDC (World Wide Developers Conference) is introduced.
Read more
Syllabus
Deep Learning Products & Services
For the course “Deep Learning for Business,” the first module is “Deep Learning Products & Services,” which starts with the lecture “Future Industry Evolution & Artificial Intelligence” that explains past, current, and future industry evolutions and how DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of future industry in the near future. The following lectures look into the hottest DL and ML products and services that are exciting the business world. First, the “Jeopardy!” winning versatile IBM Watson is introduced along with its DeepQA and AdaptWatson systems that use DL technology. Then the Amazon Echo and Echo Dot products are introduced along with the Alexa cloud based DL personal assistant that uses ASR (Automated Speech Recognition) and NLU (Natural Language Understanding) technology. The next lecture focuses on LettuceBot, which is a DL system that plants lettuce seeds with automatic fertilizer and herbicide nozzles control. Then the computer vision based DL blood cells analysis diagnostic system Athelas is introduced followed by the introduction of a classical and symphonic music composing DL system named AIVA (Artificial Intelligence Virtual Artist). As the last topic of module 1, the upcoming Apple watchOS 4 and the HomePod speaker that was presented at Apple's 2017 WWDC (World Wide Developers Conference) is introduced.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Suitable for professionals in any industry seeking a strategic advantage by using AI
Introduces fundamental AI concepts and how they can be applied to business
In-depth exploration of Deep Learning and Machine Learning technologies
Emphasis on real-world applications with examples from cutting-edge products and services
Practical experience through hands-on projects using TensorFlow Playground
Taught by industry experts with extensive experience in AI and machine learning
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Deep learning strategy for business leaders
According to learners, this course offers a solid introduction to Deep Learning concepts specifically for those interested in its business applications. Many appreciate its focus on strategic considerations, industry overview, and discussion of real-world products and services. The TensorFlow Playground projects are frequently mentioned as a helpful way to visualize neural networks without deep coding. However, a notable portion of students found the course lacked sufficient technical depth and hands-on coding practice, making it less suitable for those seeking implementation skills. It appears best for non-technical professionals or managers aiming for a strategic understanding of DL/ML.
Helpful for visualizing concepts visually.
"The TensorFlow Playground projects were a good way to visualize how NNs work, without getting bogged down in complex coding."
"The Playground demo was a nice touch."
"The Playground was a novel way to learn."
"Playing with the TensorFlow Playground was really intuitive."
Strategic view of DL/ML for business.
"This course provided a solid overview of Deep Learning concepts from a business perspective. It helped me understand the potential of DL for my company."
"Excellent introduction for managers and business leaders. The strategic considerations were particularly valuable. Highly recommend for non-technical professionals."
"I appreciated the focus on business applications of DL/ML. The modules on industry evolution and business strategy were relevant."
"This course delivered exactly what the title suggests - Deep Learning for Business. It's a strategic overview."
Some examples feel slightly dated.
"The information felt slightly dated, especially the product examples from 2017."
"Wish it covered more recent advancements."
"Still relevant basics, but context feels 2017."
High-level overview, minimal technical detail.
"The course is decent but very high-level. It focuses a lot on products and strategy but lacks the technical depth I was hoping for."
"Disappointing... There's very little on how to actually *do* anything with DL, just talk about what others are doing."
"As someone with a bit of technical background, I found this course too simplistic... the technical parts are very surface-level."
"Completely useless if you want practical skills... No real teaching on building or implementing anything."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Deep Learning for Business with these
activities:
Review the basics of linear algebra
Show steps
Reviewing the basics of linear algebra will help you understand the mathematical underpinnings of deep learning.
Browse courses on
Linear Algebra
Show steps
-
Review the basics of vectors and matrices
-
Review the basics of linear transformations
-
Review the basics of eigenvalues and eigenvectors
Watch tutorials on deep learning
Show steps
Watching tutorials on deep learning will help you learn the fundamentals of the field and get started with building your own models.
Browse courses on
Deep Learning
Show steps
-
Watch the TensorFlow tutorials
-
Watch the Coursera deep learning tutorials
-
Find additional tutorials online
Review the book Deep Learning
Show steps
Deep Learning by Goodfellow, Bengio, and Courville provides a thorough introduction to the field of deep learning, covering the fundamental concepts and algorithms. This book will greatly enhance your understanding of the topics covered in the course.
View
Deep Learning
on Amazon
Show steps
-
Read Chapter 1: Introduction
-
Read Chapter 2: Linear Regression
-
Read Chapter 3: Logistic Regression
-
Read Chapter 4: Neural Networks
-
Read Chapter 5: Convolutional Neural Networks
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice coding exercises on TensorFlow
Show steps
Practicing coding exercises on TensorFlow will help you develop the skills necessary to build and train deep learning models.
Browse courses on
TensorFlow
Show steps
-
Solve the TensorFlow Tutorial exercises
-
Complete the TensorFlow exercises on Coursera
-
Find additional exercises online
Build a deep learning model
Show steps
Building a deep learning model will give you hands-on experience with the entire process of designing, training, and evaluating a deep learning model.
Browse courses on
Deep Learning
Show steps
-
Define the problem you want to solve
-
Collect and prepare your data
-
Design and build your model
-
Train your model
-
Evaluate your model
Contribute to an open-source deep learning project
Show steps
Contributing to an open-source deep learning project will give you experience working on real-world deep learning projects and collaborating with others.
Browse courses on
Open Source
Show steps
-
Find an open-source deep learning project that interests you
-
Read the project's documentation
-
Submit a pull request
Review the basics of linear algebra
Show steps
Reviewing the basics of linear algebra will help you understand the mathematical underpinnings of deep learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the basics of vectors and matrices
- Review the basics of linear transformations
- Review the basics of eigenvalues and eigenvectors
Watch tutorials on deep learning
Show steps
Watching tutorials on deep learning will help you learn the fundamentals of the field and get started with building your own models.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Watch the TensorFlow tutorials
- Watch the Coursera deep learning tutorials
- Find additional tutorials online
Review the book Deep Learning
Show steps
Deep Learning by Goodfellow, Bengio, and Courville provides a thorough introduction to the field of deep learning, covering the fundamental concepts and algorithms. This book will greatly enhance your understanding of the topics covered in the course.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1: Introduction
- Read Chapter 2: Linear Regression
- Read Chapter 3: Logistic Regression
- Read Chapter 4: Neural Networks
- Read Chapter 5: Convolutional Neural Networks
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice coding exercises on TensorFlow
Show steps
Practicing coding exercises on TensorFlow will help you develop the skills necessary to build and train deep learning models.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Solve the TensorFlow Tutorial exercises
- Complete the TensorFlow exercises on Coursera
- Find additional exercises online
Build a deep learning model
Show steps
Building a deep learning model will give you hands-on experience with the entire process of designing, training, and evaluating a deep learning model.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Define the problem you want to solve
- Collect and prepare your data
- Design and build your model
- Train your model
- Evaluate your model
Contribute to an open-source deep learning project
Show steps
Contributing to an open-source deep learning project will give you experience working on real-world deep learning projects and collaborating with others.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source deep learning project that interests you
- Read the project's documentation
- Submit a pull request
Career center
Learners who complete Deep Learning for Business will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
The Deep Learning for Business course can be very useful to you as a Machine Learning Engineer, as it provides foundational knowledge of deep learning, which is a key technology in this field.
Data Scientist
Data Scientist
As a Data Scientist, your focus is on utilizing AI to derive new insights. The technical foundational knowledge you will learn in Deep Learning for Business can help you make more effective and impactful contributions in this role.
Consultant
Consultant
Consultants can benefit from the Deep Learning for Business course as it can help provide a foundation in relevant technologies and industry best practices.
Software Engineer
Software Engineer
Software Engineers can benefit from taking the Deep Learning for Business course to build foundational knowledge of how deep learning and machine learning technologies function and how to apply them in practice.
Database Administrator
Database Administrator
Deep Learning for Business can be very useful for Database Administrators because it can help you build a foundation in the technological principles driving force behind many modern database systems.
Artificial Intelligence Specialist
Artificial Intelligence Specialist
For those looking to specialize in Artificial Intelligence, the Deep Learning for Business course may be useful to you because it can help you develop a foundational understanding of this field.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you may be responsible for building models to help make decisions. Deep Learning for Business can be useful because it helps build the foundational knowledge needed to utilize artificial intelligence in this process.
Entrepreneur
Entrepreneur
For those interested in starting their own company, this Deep Learning for Business course may be useful because it provides foundational knowledge of technologies that are becoming increasingly common in modern businesses.
Product Manager
Product Manager
As a Product Manager, you may be responsible for overseeing the development of successful products within your organization. Deep Learning for Business can be very helpful to you because it helps you build a foundation in the key underlying technologies relevant to many products.
Analyst
Analyst
As an Analyst, the technical foundations learned in Deep Learning for Business can equip you with skills to extract key information from data to derive insights.
Data Analyst
Data Analyst
The Deep Learning for Business course may be useful for Data Analysts, as it can aid in developing some of the foundational knowledge needed to help derive insights from data.
Project Manager
Project Manager
As a Project Manager, you may collaborate in leading and managing software development projects. Deep Learning for Business can be useful to you because it helps you build a strong understanding of the underlying technologies of software products.
Business Intelligence Analyst
Business Intelligence Analyst
Deep Learning for Business can be useful as a Business Intelligence Analyst because it can help you to establish a foundation in technologies that are becoming increasingly relevant to the field.
Engineer
Engineer
Deep Learning for Business can be useful for Engineers because it can aid in building a foundation in the technological principles driving force behind many modern systems.
Researcher
Researcher
As a Researcher, you may contribute to research-based development. This Deep Learning for Business course may be useful to you because it can help you build a foundation in the key underlying technologies.
For more career information including salaries, visit:
OpenCourser.com/course/89wikk/deep
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Deep Learning for Business.
Save
Comprehensive reference on deep learning. It covers the theoretical foundations of deep learning, as well as practical aspects such as building and training neural networks. The book also includes numerous exercises and examples to help readers apply their knowledge.
Provides a comprehensive overview of artificial intelligence. It covers the theoretical foundations of AI, as well as practical aspects such as building and training AI systems. The book also includes numerous exercises and examples to help readers apply their knowledge.
Comprehensive guide to deep learning with Python. It covers the theoretical foundations of deep learning, as well as practical aspects such as building and training neural networks. The book also includes numerous exercises and examples to help readers apply their knowledge.
Provides a comprehensive overview of machine learning. It covers the theoretical foundations of machine learning, as well as practical aspects such as building and training machine learning models. The book also includes numerous exercises and examples to help readers apply their knowledge.
Provides a comprehensive overview of deep reinforcement learning. It covers the theoretical foundations of deep reinforcement learning, as well as practical aspects such as building and training deep reinforcement learning agents. The book also includes numerous exercises and examples to help readers apply their knowledge.
Provides a comprehensive overview of deep learning for natural language processing. It covers the theoretical foundations of deep learning, as well as practical aspects such as building and training neural networks for natural language processing tasks. The book also includes numerous exercises and examples to help readers apply their knowledge.
Provides a comprehensive overview of deep learning for computer vision. It covers the theoretical foundations of deep learning, as well as practical aspects such as building and training deep neural networks for computer vision tasks.
Provides a practical guide to machine learning with Python. It covers the basics of machine learning, as well as practical aspects such as data preprocessing, feature engineering, and model selection. The book also includes numerous exercises and examples to help readers apply their knowledge.
Save
Provides a comprehensive guide to deep learning with R. It covers the theoretical foundations of deep learning, as well as practical aspects such as building and training neural networks. The book also includes numerous exercises and examples to help readers apply their knowledge.
Provides an accessible introduction to machine learning with Python. It covers the basics of machine learning, as well as practical aspects such as building and training machine learning models. The book also includes numerous exercises and examples to help readers apply their knowledge.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/89wikk/deep
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Level
Introductory
Via
Coursera
Institution
Yonsei University
Instructor
Jong-Moon Chung
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
magyar nyelv
Română
Bahasa Indonesia
ไทย
қазақша
اُردُو,
فارسی
বাংলা
tiếng Việt
پښتو
آذربایجان دیلی
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser