Applied AI with DeepLearning from Coursera

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This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.

IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018

Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide.

Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well.

If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

What's inside

Syllabus

Introduction to deep learning

DeepLearning Frameworks

DeepLearning Applications

Scaling and Deployment

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and other disciplines

Uses real-life examples from IoT, Financial Marked Data, Literature or Image Databases to build models using Keras

Introduces popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML

Teaches how to scale artificial brains using Kubernetes, Apache Spark and GPUs

Taught by Romeo Kienzler, Max Pumperla, Ilja Rasin, Niketan Pansare, and Tom Hanlon who are experts in Deep Learning

Requires some coding skills although preferably Python programming skills

Advises students to take other courses first as prerequisites

Reviews summary

Well-received course on applied ai with deep learning

According to students, this highly rated course on Applied AI with Deep Learning teaches you how to use DeepLearning frameworks such as TensorFlow, Keras, and PyTorch. It offers engaging assignments and covers a wide range of topics, from image recognition to natural language processing. However, some students noted that the assignments could be more challenging and that the course material is not always up-to-date. Overall, this course is a solid choice for learners looking to gain practical experience in Deep Learning with a focus on real-world applications.

The course covers a wide range of topics in Deep Learning, including:

"NLP, digital signal processing gives us immense exposure to wide applications of Tensorflow."

"Even though this course covers quite a bit of breath - in terms of implementation frameworks."

Learners appreciate the course's engaging assignments and practical approach to Deep Learning.

"I learned how to utilize SystemML and DeepLearning4j to train different ML models on Apache Spark clusters."

"This course is very useful, it reviews in a simple but didactic way the main characteristics of AI within Deep learning, very useful for time series and graphic processing."

"I learned many things from this course."

A number of students have reported technical issues with the course, such as problems with the IBM cloud service and difficulties running the code locally.

"It is difficult to fully understand the contents of the lesson, too many theories and not yet associated with practical problems."

"I respect the knowledge the Instructors have. But knowing something doesn't necessarily mean that you're good at teaching it."

"I found this to be an excellent introduction to Deep Learning Frameworks. The fact that we cover images, NLP, digital signal processing gives us immense exposure to wide applications of Tensorflow."

Some students have reported that the course material is outdated and that the assignments do not always work as expected.

"The content is excellent, but I had troubles to could run some of the code locally (because I had a different set up/libraries installed locally)."

"Some of the videos use python 2, which is even no longer available in the IBM lab."

"the assignments provided in the course had several issues with library versions."

While some students found the assignments to be engaging, others felt that they were too easy and not challenging enough.

"I wish it had some more difficult assignments requiring you to code your own neural networks"

"Assignments are a bit too simple"

"the assignments were not good enough to test what was taught."

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 Applied AI with DeepLearning with these activities:

Create a collection of deep learning resources

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Organize and curate a repository of valuable resources, including articles, tutorials, and tools, to support your learning journey.

Browse courses on Resource Management

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Identify and gather relevant resources from various sources.
Categorize and organize the resources based on topics or themes.
Use a platform or tool to create a central repository for your collection.

Read "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

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Gain a comprehensive understanding of deep learning concepts and algorithms from one of the leading textbooks in the field.

View Deep Learning on Amazon

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Acquire the book or access it through online resources.
Read each chapter thoroughly, taking notes and highlighting important concepts.
Solve the exercises and review the solutions to test your understanding.

Connect with professionals in the field of deep learning

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Gain valuable insights, guidance, and support from experienced professionals to enhance your learning journey.

Browse courses on Networking

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Attend industry events and conferences.
Reach out to individuals working in deep learning through social media or professional platforms.
Seek guidance from professors, researchers, or experts in the field.

Five other activities

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Watch instructional videos on deep neural networks

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Explore the foundational concepts and familiarize yourself with the basic building blocks of deep learning.

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Find reputable online resources and tutorials.
Break down complex concepts into smaller, manageable chunks.
Take notes and review them regularly.

Solve practice problems on deep learning models

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Develop your problem-solving skills by applying deep learning techniques to practical scenarios.

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Identify online platforms or textbooks with practice problems.
Practice regularly to enhance your understanding of model building and optimization.
Review your solutions and seek feedback from peers or instructors.

Participate in deep learning workshops or hackathons

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Immerse yourself in practical deep learning applications and collaborate with peers to exchange knowledge and ideas.

Browse courses on Hands-On Learning

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Identify relevant workshops or hackathons in your area.
Prepare for the event by reviewing the topics and requirements.
Actively participate in hands-on activities and discussions.
Network with other participants and professionals.

Build a deep learning model for a real-world problem

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Apply your knowledge in a practical setting by building and deploying your own deep learning solution.

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Define a specific problem or challenge you want to address.
Gather and prepare your data.
Choose and implement an appropriate deep learning model.
Train and evaluate your model.
Deploy your model and monitor its performance.

Share your knowledge by mentoring junior deep learning learners

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Reinforce your understanding by explaining concepts to others and support the development of the deep learning community.

Browse courses on Teaching

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Identify opportunities to mentor junior learners through programs or online platforms.
Prepare materials and lesson plans to facilitate effective learning.
Provide guidance, support, and feedback to your mentees.

Career center

Learners who complete Applied AI with DeepLearning will develop knowledge and skills that may be useful to these careers:

Deep Learning Researcher

Deep Learning Researchers conduct cutting-edge research on deep learning algorithms and models. They develop new techniques and theories to advance the field of artificial intelligence. This course provides a great foundation for those seeking to pursue a career in Deep Learning research.

See salaries and explore the career path for Deep Learning Researcher

Machine Learning Architect

Machine Learning Architects design and build machine learning systems and solutions. This course could be useful as it covers the fundamentals of Deep Learning and its practical applications in machine learning architecture.

See salaries and explore the career path for Machine Learning Architect

Computer Vision Engineer

Computer Vision Engineers develop and implement computer vision systems that can interpret and understand visual data. This course would be beneficial as it covers Deep Learning models and their applications in image recognition.

See salaries and explore the career path for Computer Vision Engineer

Natural Language Processing Engineer

Natural Language Processing Engineers build systems that can understand and generate human language. This course delves into Deep Learning models and their applications in natural language processing.

See salaries and explore the career path for Natural Language Processing Engineer

Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and maintain AI systems. This course may be useful as it provides a comprehensive overview of Deep Learning and its applications in AI engineering.

See salaries and explore the career path for Artificial Intelligence Engineer

Data Scientist

Data Scientists build and train mathematical models using machine learning algorithms to make predictions about future events. They work with massive datasets to extract valuable insights for businesses. This course may be useful as it provides a deep dive into Deep Learning models and their applications in various domains.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers work on projects that use artificial intelligence. They take research and development from a conceptual phase into the real world. This course may be useful as it dives into the fundamentals of Deep Learning, the most popular DeepLearning Frameworks, and their applications in real world examples.

See salaries and explore the career path for Machine Learning Engineer

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve problems in business and industry. This course may be helpful as it provides a foundation in Deep Learning and its potential applications in operations research.

See salaries and explore the career path for Operations Research Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course may be beneficial as it provides an introduction to Deep Learning and its applications in finance.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians collect, analyze, and interpret data to draw meaningful conclusions. This course may be beneficial as it provides a solid foundation in Deep Learning and its potential applications in statistics.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts use data and analytics to solve business problems and improve decision-making. This course could be beneficial as it covers the basics of Deep Learning and its potential applications in business.

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Data Analyst

Data Analysts clean, process, and analyze data to identify trends and patterns. This course may be useful as it provides an understanding of Deep Learning models and their applications in data analysis.

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Software Engineer

Software Engineers design, develop, and test software applications. This course may be useful as it provides a comprehensive overview of Deep Learning concepts and their practical applications in software development.

See salaries and explore the career path for Software Engineer

Risk Analyst

Risk Analysts assess and manage risks in various industries. This course may be useful as it provides an overview of Deep Learning and its potential applications in risk management.

See salaries and explore the career path for Risk Analyst

Product Manager

Product Managers are responsible for developing and launching new products. This course could be helpful as it provides insights into Deep Learning and its potential impact on product development.

See salaries and explore the career path for Product Manager

Applied AI with DeepLearning

What's inside

Syllabus

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Reviews summary

Well-received course on applied ai with deep learning

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