Deep Learning for Business from Coursera

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.

Business with Deep Learning & Machine Learning

The second module “Business with Deep Learning & Machine Learning” first focuses on various business considerations based on changes to come due to DL (Deep Learning) and ML (Machine Learning) technology in the lecture “Business Considerations in the Machine Learning Era.” In the following lecture “Business Strategy with Machine Learning & Deep Learning” explains the changes that are needed to be more successful in business, and provides an example of business strategy modeling based on the three stages of preparation, business modeling, and model rechecking & adaptation. The next lecture “Why is Deep Learning Popular Now?” explains the changes in recent technology and support systems that enable the DL systems to perform with amazing speed, accuracy, and reliability. The last lecture “Characteristics of Businesses with DL & ML” first explains DL and ML based business characteristics based on data types, followed by DL & ML deployment options, the competitive landscape, and future opportunities are also introduced.

Deep Learning Computing Systems & Software

The third module “Deep Learning Computing Systems & Software” focuses on the most significant DL (Deep Learning) and ML (Machine Learning) systems and software. Except for the NVIDIA DGX-1, the introduced DL systems and software in this module are not for sale, and therefore, may not seem to be important for business at first glance. But in reality, the companies that created these systems and software are indeed the true leaders of the future DL and ML business era. Therefore, this module introduces the true state-of-the-art level of DL and ML technology. The first lecture introduces the most popular DL open source software TensorFlow, CNTK (Cognitive Toolkit), Keras, Caffe, Theano, and their characteristics. Due to their popularly, strong influence, and diverse capabilities, the following lectures introduce the details of Google TensorFlow and Microsoft CNTK. Next, NVIDIA’s supercomputer DGX-1, that has fully integrated customized DL hardware and software, is introduced. In the following lectures, the most interesting competition of human versus machine is introduced in the Google AlphaGo lecture, and in the ILSVRC (ImageNet Large Scale Visual Recognition Challenge) lecture, the results of competition between cutting edge DL systems is introduced and the winning performance for each year is compared.

Basics of Deep Learning Neural Networks

The module “Basics of Deep Learning Neural Networks” first focuses on explaining the technical differences of AI (Artificial Intelligence), ML (Machine Learning), and DL (Deep Learning) in the first lecture titled “What is DL (Deep Learning) and ML (Machine Learning).” In addition, the characteristics of CPUs (Central Processing Units) and GPUs (Graphics Processing Units) used in DL as well as the representative computer performance units of FLOPS (FLoating-Point Operations Per Second) and IPS (Instructions Per Second) are introduced. Next, in the NN (Neural Network) lecture, the biological neuron (nerve cell) and its signal transfer is introduced followed by an ANN (Artificial Neural Network) model of a neuron based on a threshold logic unit and soft output activation functions is introduced. Then the extended NN technologies that uses MLP (Multi-Layer Perceptron), SoftMax, and AutoEncoder are explained. In the last lecture of the module, NN learning based on backpropagation is introduced along with the learning method types, which include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Deep Learning with CNN & RNN

The module “Deep Learning with CNN & RNN” focuses on CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) technology that enable DL (Deep Learning). First the lectures introduce how CNNs used in image/video recognition, recommender systems, natural language processing, and games (like Chess and Go) are made possible through processing in the convolutional layer and feature maps. The lecture also introduces how CNNs use subsampling (pooling), LCN (Local Contrast Normalization), dropout, ensemble, and bagging technology to become more efficient, reliable, robust, and accurate. Next, the lectures introduce how DL with RNN is used in speech recognition (as in Apple's Siri, Google’s Voice Search, and Samsung's S Voice), handwriting recognition, sequence data analysis, and program code generation. Then the details of RNN technologies are introduced, which include S2S (Sequence to Sequence) learning, forward RNN, backward RNN, representation techniques, context based projection, and representation with attention. As the last part of the module, the early model of RNN, which is the FRNN (Fully Recurrent NN), and the currently popular RNN model LSTM (Long Short-Term Memory) is introduced.

Deep Learning Project with TensorFlow Playground

The module “Deep Learning Project with TensorFlow Playground” focuses on four NN (Neural Network) design projects, where experience on designing DL (Deep Learning) NNs can be gained using a fun and powerful application called the TensorFlow Playground. The lectures first teach how to use the TensorFlow Playground, which is followed by guidance on three projects so you can easily buildup experience on using the TensorFlow Playground system. Then in Project 4 a “DL NN Design Challenge” is given, where you will need to make the NN “Deeper” by adding hidden layers and neurons to satisfy the classification objectives. The knowledge you obtained in the lecture of Modules 1~5 will be used in these projects.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Engaging deep learning business overview

Learners say this course provides an engaging overview of Deep Learning from a business perspective. They appreciate the clear and detailed explanations, as well as the well-paced delivery of the material. The informative lectures and helpful resources help beginners build a strong foundation in the subject. However, some learners felt that the course could be more concise and focused on the practical applications of Deep Learning within specific business contexts.

Instructor is knowledgeable and engaging.

"Amazing teacher, very well explained"

"Mil gracias maestro Jong-Moon Chung"

"I learnt so much from this course. It was great and the teacher explained evrything in a great way."

Course provides a strong foundation in Deep Learning.

"Wonderful foundational knowledge base for those interested in ML but don't know where to start."

"It was very informative, the instructor paces the information very well, & I love the resources at the end of every lecture."

" It is such a great and fullfilling course. It enriches my perspective on business providing a great deal of technology related concepts on Deep Learning's interesting sphere ."

Concepts are explained clearly.

"Muy bien dictado. Se entienden con claridad los conceptos y las explicaciones."

"The instructor gave details explanation step by step for easy understanding."

"Its was such a good experience. Such a nice assignments and lectures. I learned lots of think in deep learning, machine learning and artificial intelligence"

Course lacks focus and clarity on target audience.

"I feel like this course didn't find its niche."

"Only partly convincing. Focus on learning facts and not on understanding concepts."

"But if you want to take this course, stop at week 4."

Course includes too much detail in some sections.

"For those who have not study any course about Machine Learning, it would be too difficult to understand the ideas."

"Even though this is a course for beginners, you won't be able to understand week 5 unless you already know A LOT about Neural Networks."

"The course is too short but try to cover too much context."

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

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Reviewing the basics of linear algebra will help you understand the mathematical underpinnings of deep learning.

Browse courses on Linear Algebra

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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

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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

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Watch the TensorFlow tutorials
Watch the Coursera deep learning tutorials
Find additional tutorials online

Review the book Deep Learning

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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

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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

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Practice coding exercises on TensorFlow

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Practicing coding exercises on TensorFlow will help you develop the skills necessary to build and train deep learning models.

Browse courses on TensorFlow

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Solve the TensorFlow Tutorial exercises
Complete the TensorFlow exercises on Coursera
Find additional exercises online

Build a deep learning model

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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

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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

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Contributing to an open-source deep learning project will give you experience working on real-world deep learning projects and collaborating with others.

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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

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Engineer