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Alex Aklson,
Romeo Kienzler,
Samaya Madhavan,
and
JEREMY NILMEIER
The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
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What's inside
Syllabus
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
You will also learn about the fundamentals of Deep Learning.
Read more
Syllabus
Introduction
In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models.
You will also learn about the fundamentals of Deep Learning.
Read more
Supervised Learning Models
In this module, you will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning. You will also learn about the popular MNIST database. Finally, you will learn how to build a Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow.
Supervised Learning Models (Cont'd)
In this module, you will learn about the recurrent neural network model, and special type of a recurrent neural network, which is the Long Short-Term Memory model. Also, you will learn about the Recursive Neural Tensor Network theory, and finally, you will apply recurrent neural networks to language modelling.
Unsupervised Deep Learning Models
In this module, you will learn about the applications of unsupervised learning. You will learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM. Finally, you will apply Restricted Boltzmann Machines to build a recommendation system.
Unsupervised Deep Learning Models (Cont'd) and scaling
In this module, you will mainly learn about autoencoders and their architecture.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Demonstrates how Deep Learning can be applied to real-world problems, making it highly relevant in industry
Uses a popular library, TensorFlow, which is widely used in the industry and research community
Covers a wide range of Deep Learning models, from Convolutional Neural Networks to Recurrent Neural Networks
Taught by experienced instructors, Romeo Kienzler, Alex Aklson, Samaya Madhavan, and JEREMY NILMEIER, who are recognized for their work in the field
Requires learners to come in with some prior knowledge of Python and Machine Learning
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Reviews summary
Comprehensively effective deep learning models with tensorflow
learners say that this course largely positive for understanding the creation of deep learning models with TensorFlow. Engaging assignments include coding neural networks and working with autoencoders. While the information provided largely positive, learners should be aware that some of the content and code is dependent on TensorFlow version 1.x and might need to be updated.
According to reviewers, the course provides clear explanations and covers a wide range of topics.
"The detail of prsenetation is awsome and make learning interesting."
"This is just introductory course, wish to see more content and in details concept."
"I would like to thank the lecturer for this fruitful course."
"I have enjoyed it, Thanks a lot!"
Learners have expressed concerns about the quality and accuracy of code examples in the course. Some learners have reported issues with errors and bugs in the code.
"I get some basic Idea about deep learning"
"The codes should be provided with Tensorflow 2.0."
"the code is SEVERELY out of date."
"This course was very informative and the labs are really well written.... however the code is SEVERELY out of date."
Unfortunately, some of the course content is outdated, including code examples that are based on TensorFlow 1.x and no longer applicable to the current version.
"The Material needs to be updated to Tensorflow 2.0 at least."
"The tensorflow version is outdated"
"The code in the course is obsolete using an old version of TF."
"All the codes are for tensorflow version 1 and not 2 which essentially makes them outdated"
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 Building Deep Learning Models with TensorFlow with these
activities:
Find a mentor who can provide guidance on your deep learning journey
Show steps
Provides you with support and guidance from an experienced professional in the field.
Browse courses on
Deep Learning
Show steps
-
Identify your areas of interest within deep learning.
-
Reach out to professionals in your network or attend industry events to find potential mentors.
-
Interview potential mentors and choose someone who aligns with your goals and values.
Review key concepts from linear algebra
Show steps
Refreshes your understanding of linear algebra, which is essential for understanding deep learning models.
Browse courses on
Linear Algebra
Show steps
-
Review basic matrix operations, such as addition, subtraction, and multiplication.
-
Solve systems of linear equations using Gaussian elimination.
-
Find eigenvalues and eigenvectors of matrices.
Practice implementing simple neural networks from scratch
Show steps
Develops a deeper understanding of the inner workings of neural networks.
Browse courses on
Neural Networks
Show steps
-
Create a simple feedforward neural network with one hidden layer.
-
Train the network on a dataset of your choice.
-
Evaluate the performance of the network on a test set.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow a tutorial on building a deep learning model for a specific task
Show steps
Provides practical experience in applying deep learning to real-world problems.
Browse courses on
Deep Learning
Show steps
-
Choose a specific task that you want to solve using deep learning.
-
Find a tutorial that provides step-by-step instructions on how to build a model for that task.
-
Follow the tutorial and build the model.
Attend a workshop on deep learning
Show steps
Provides an opportunity to learn from experts in the field and network with other students.
Browse courses on
Deep Learning
Show steps
-
Find a workshop on deep learning that aligns with your interests.
-
Register for the workshop and attend the sessions.
-
Participate in discussions and ask questions to enhance your understanding.
Write a blog post explaining the different types of deep learning models
Show steps
Helps you solidify your understanding of deep learning models and their applications.
Browse courses on
Deep Learning
Show steps
-
Research different types of deep learning models.
-
Choose three different types of models and explain their strengths and weaknesses.
-
Provide examples of real-world applications for each type of model.
Mentor a junior student or colleague who is learning deep learning
Show steps
Helps you reinforce your understanding of deep learning by teaching others.
Browse courses on
Deep Learning
Show steps
-
Find a junior student or colleague who is interested in learning deep learning.
-
Offer to mentor them and provide guidance on their learning journey.
-
Meet with them regularly to answer their questions and provide feedback on their progress.
Find a mentor who can provide guidance on your deep learning journey
Show steps
Provides you with support and guidance from an experienced professional in the field.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify your areas of interest within deep learning.
- Reach out to professionals in your network or attend industry events to find potential mentors.
- Interview potential mentors and choose someone who aligns with your goals and values.
Review key concepts from linear algebra
Show steps
Refreshes your understanding of linear algebra, which is essential for understanding deep learning models.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review basic matrix operations, such as addition, subtraction, and multiplication.
- Solve systems of linear equations using Gaussian elimination.
- Find eigenvalues and eigenvectors of matrices.
Practice implementing simple neural networks from scratch
Show steps
Develops a deeper understanding of the inner workings of neural networks.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Create a simple feedforward neural network with one hidden layer.
- Train the network on a dataset of your choice.
- Evaluate the performance of the network on a test set.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow a tutorial on building a deep learning model for a specific task
Show steps
Provides practical experience in applying deep learning to real-world problems.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Choose a specific task that you want to solve using deep learning.
- Find a tutorial that provides step-by-step instructions on how to build a model for that task.
- Follow the tutorial and build the model.
Attend a workshop on deep learning
Show steps
Provides an opportunity to learn from experts in the field and network with other students.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a workshop on deep learning that aligns with your interests.
- Register for the workshop and attend the sessions.
- Participate in discussions and ask questions to enhance your understanding.
Write a blog post explaining the different types of deep learning models
Show steps
Helps you solidify your understanding of deep learning models and their applications.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Research different types of deep learning models.
- Choose three different types of models and explain their strengths and weaknesses.
- Provide examples of real-world applications for each type of model.
Mentor a junior student or colleague who is learning deep learning
Show steps
Helps you reinforce your understanding of deep learning by teaching others.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a junior student or colleague who is interested in learning deep learning.
- Offer to mentor them and provide guidance on their learning journey.
- Meet with them regularly to answer their questions and provide feedback on their progress.
Career center
Learners who complete Building Deep Learning Models with TensorFlow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design and build machine learning models to solve real-world problems. This course can help you develop the skills needed to become a machine learning engineer. It provides hands-on experience with TensorFlow, a popular deep learning library, and covers topics such as data preprocessing, feature engineering, model training, and evaluation.
Data Scientist
Data Scientist
Data scientists use advanced analytical techniques to extract insights from data. Deep learning is a powerful tool for data scientists, and this course can help you build a strong foundation in the field. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. You'll also gain hands-on experience with TensorFlow, a popular deep learning library.
Researcher
Researcher
Researchers use scientific methods to study different aspects of the world. Deep learning is a powerful tool for researchers, and this course can help you build a foundation in the field. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. You'll also gain hands-on experience with TensorFlow, a popular deep learning library.
Statistician
Statistician
Statisticians use data to solve problems and make informed decisions. This course can help you build a foundation in deep learning, a powerful technique used in statistics. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of statistics.
Data Analyst
Data Analyst
Data analysts use data to solve business problems and make informed decisions. This course can help you develop the skills needed to analyze and interpret data using deep learning. It provides hands-on experience with TensorFlow, a popular deep learning library, and covers topics such as data preprocessing, feature engineering, and model evaluation.
Data Engineer
Data Engineer
Data engineers design, build, and maintain data pipelines. This course can help you build a foundation in deep learning, a powerful technique used in data engineering. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of data engineering.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can help you build a foundation in deep learning, a powerful technique used in quantitative finance. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the financial industry.
Financial Analyst
Financial Analyst
Financial analysts use data to make investment decisions and advise clients. This course can help you build a foundation in deep learning, a powerful technique used in financial analysis and forecasting. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the financial industry.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software systems. This course can help you build a foundation in deep learning, a powerful technique used in software development. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in software development.
Business Analyst
Business Analyst
Business analysts use data to help businesses make better decisions. This course can help you build a foundation in deep learning, a powerful technique used in business analysis. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of business analysis.
Systems Analyst
Systems Analyst
Systems analysts design and implement computer systems. This course can help you build a foundation in deep learning, a powerful technique used in systems analysis and design. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of systems analysis.
Web Developer
Web Developer
Web developers design and develop websites. This course can help you build a foundation in deep learning, a powerful technique used in web development. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of web development.
Product Manager
Product Manager
Product managers develop and manage products. This course can help you build a foundation in deep learning, a powerful technique used in product management. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of product management.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk in the insurance and finance industries. This course can help you build a foundation in deep learning, a powerful technique used in risk assessment and modeling. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the actuarial field.
Teacher
Teacher
Teachers educate students in various subjects. This course can help you build a foundation in deep learning, a powerful technique used in education. It covers topics such as supervised and unsupervised learning, neural networks, and autoencoders, which are essential for understanding and applying deep learning in the field of education.
For more career information including salaries, visit:
OpenCourser.com/course/m9c4qb/building
Reading list
We've selected eight 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
Building Deep Learning Models with TensorFlow.
Save
Provides a comprehensive overview of deep learning, covering the fundamental concepts and algorithms used in the field. It is an excellent resource for anyone looking to get started with deep learning or to supplement their knowledge of the subject.
Provides a comprehensive introduction to deep learning, covering the fundamental concepts, algorithms, and techniques used in the field. It is an excellent resource for anyone looking to get started with deep learning or to supplement their knowledge of the subject.
Provides a comprehensive overview of deep learning for healthcare, covering the fundamental concepts and algorithms used in the field. It is an excellent resource for anyone looking to get started with deep learning for healthcare or to supplement their knowledge of the subject.
Provides a comprehensive overview of recurrent neural networks, covering the fundamental concepts and algorithms used in the field. It is an excellent resource for anyone looking to get started with recurrent neural networks or to supplement their knowledge of the subject.
Provides a comprehensive overview of natural language processing with deep learning, covering the fundamental concepts and algorithms used in the field. It is an excellent resource for anyone looking to get started with natural language processing with deep learning or to supplement their knowledge of the subject.
Provides a comprehensive overview of deep learning for natural language processing. It covers the fundamental concepts and algorithms used in the field, and it also provides a number of case studies that demonstrate how deep learning can be used to solve real-world NLP problems.
Provides a practical guide to using deep learning for real-world problems. It covers the fundamental concepts and algorithms used in the field, and it also provides a number of case studies that demonstrate how deep learning can be used to solve real-world problems.
Provides a practical guide to using Scikit-Learn, Keras, and TensorFlow for machine learning. It covers the basics of machine learning, including how to prepare data, train models, and evaluate performance.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m9c4qb/building
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Effort
4-5 hours/week
Level
Intermediate
Via
Coursera
Institution
IBM
Instructors
Alex Aklson
Romeo Kienzler
Samaya Madhavan
JEREMY NILMEIER
Language
English
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Good to know
Demonstrates how Deep Learning can be applied to real-world problems, making it highly relevant in industry
Uses a popular library, TensorFlow, which is widely used in the industry and research community
Covers a wide range of Deep Learning models, from Convolutional Neural Networks to Recurrent Neural Networks
Taught by experienced instructors, Romeo Kienzler, Alex Aklson, Samaya Madhavan, and JEREMY NILMEIER, who are recognized for their work in the field
Requires learners to come in with some prior knowledge of Python and Machine Learning
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