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Saeed Aghabozorgi,
Romeo Kienzler,
and
Samaya Madhavan
According to Indeed, machine learning engineer salaries currently start at USD 100,809 and top out at just over USD 254,000.
Gain advanced Keras and TensorFlow 2.x techniques you need to build and optimize machine learning models. In this course, practice techniques for deep learning, reinforcement learning, generative models, and sequential data handling that will prepare you to tackle complex real-world challenges.
Read more
According to Indeed, machine learning engineer salaries currently start at USD 100,809 and top out at just over USD 254,000.
Gain advanced Keras and TensorFlow 2.x techniques you need to build and optimize machine learning models. In this course, practice techniques for deep learning, reinforcement learning, generative models, and sequential data handling that will prepare you to tackle complex real-world challenges.
Read more
According to Indeed, machine learning engineer salaries currently start at USD 100,809 and top out at just over USD 254,000.
Gain advanced Keras and TensorFlow 2.x techniques you need to build and optimize machine learning models. In this course, practice techniques for deep learning, reinforcement learning, generative models, and sequential data handling that will prepare you to tackle complex real-world challenges.
You’ll begin by learning about Keras's advanced features, including its functional API used to design complex models. You’ll then learn how to create custom layers and models to tailor solutions to unique challenges and seamlessly integrate Keras with TensorFlow 2.x for enhanced functionality.
Next, you’ll use Keras to develop advanced convolutional neural networks (CNNs) that can solve complex computer vision tasks. You’ll apply data augmentation to improve model generalization, implement transfer learning with pre-trained models, and leverage TensorFlow for advanced image processing. You’ll also explore transpose convolution
Then, learn how to build and train advanced Transformers using Keras for sequential data tasks, including time series prediction. You’ll gain hands-on experience developing Transformer-based models for text generation and explore how to utilize TensorFlow to manage sequential data effectively.
Then you’ll dive into unsupervised learning with Keras. You’ll build and train autoencoders, experiment with cutting-edge diffusion models, and develop generative adversarial networks (GANs). You’ll also learn to integrate TensorFlow for advanced unsupervised learning tasks and expand your expertise in generative modeling techniques.
You’ll master advanced Keras techniques for model development by creating custom training loops and optimizing model performance. You’ll explore hyperparameter tuning using Keras Tuner and leverage TensorFlow for enhanced model optimization and custom training workflows.
In the final module, you’ll explore reinforcement learning and its applications in Keras. You’ll implement Q-Learning algorithms and develop deep Q-networks (DQNs) to tackle advanced reinforcement learning tasks, gaining practical experience with this powerful AI technique.
By the end of this course, you’ll have the knowledge and skills to build and optimize advanced models using Keras and TensorFlow 2.x, tackling challenges in computer vision, NLP, reinforcement learning, and generative modeling.
Register for this course and see more details by visiting:
OpenCourser.com/course/l7tnxk/deep
What's inside
Learning objectives
- Create custom layers and models in keras and integrate keras with tensorflow 2.x
- Develop advanced convolutional neural networks (cnns) using keras
- Develop transformer models for sequential data and time series prediction
- Explain key concepts of unsupervised learning in keras, deep q-networks (dqns), and reinforcement learning
- Create custom layers and models in keras and integrate keras with tensorflow 2.x
- Develop advanced convolutional neural networks (cnns) using keras
- Develop transformer models for sequential data and time series prediction
- Explain key concepts of unsupervised learning in keras, deep q-networks (dqns), and reinforcement learning
Syllabus
Module 1: Advanced Keras Functionalities
Welcome to the Course
Video: Course Introduction
Reading: Course Overview
Read more
Syllabus
Module 1: Advanced Keras Functionalities
Welcome to the Course
Video: Course Introduction
Reading: Course Overview
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Covers advanced techniques in Keras and TensorFlow 2.x, which are essential tools for building and optimizing machine learning models used in various industries
Explores advanced convolutional neural networks (CNNs) and data augmentation, which are fundamental for solving complex computer vision tasks and improving model generalization
Teaches how to build and train advanced Transformers using Keras for sequential data tasks, including time series prediction and text generation, which are key skills in NLP
Dives into unsupervised learning with Keras, covering autoencoders, diffusion models, and generative adversarial networks (GANs), which are cutting-edge techniques in generative modeling
Introduces reinforcement learning and its applications in Keras, implementing Q-Learning algorithms and deep Q-networks (DQNs) to tackle advanced reinforcement learning tasks
Requires familiarity with Keras and TensorFlow, so learners without prior experience may need to acquire foundational knowledge before taking this course
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Reviews summary
Advanced deep learning with keras & tensorflow
According to learners, this course provides a solid foundation in advanced Keras and TensorFlow techniques, helping students build and optimize models. Reviewers frequently praise the coverage of relevant advanced topics such as Transformers, Generative Adversarial Networks, and Reinforcement Learning. The practical labs and hands-on projects are highlighted as particularly helpful for applying concepts. While many find the content valuable, some students note that the course moves quite fast and assumes prior knowledge in deep learning and Python, suggesting it's best suited for those with existing foundations.
Detailed look at Keras with TF integration.
"The course is heavily focused on Keras with TensorFlow 2.x integration."
"It clearly shows how to integrate Keras models with TensorFlow's lower-level features."
"Great focus on the Keras Functional and Subclassing APIs for building complex models."
Provides hands-on coding experience.
"The labs were incredibly helpful for applying the concepts taught in the videos."
"Working through the practical project felt rewarding and consolidated my learning."
"I liked the labs implementing custom layers and the functional API, very practical."
"The labs provided a solid way to practice the complex topics covered."
Covers a wide range of modern DL concepts.
"I appreciated the modules on Transformers and GANs, they are very relevant today."
"The course does a good job introducing advanced architectures like Diffusion Models using Keras."
"Getting hands-on experience with Reinforcement Learning in Keras was a highlight and well-explained intro."
"Explores advanced CNN techniques like transpose convolution which is very useful."
Fast pace covering many topics.
"While comprehensive, the course sometimes feels like it's just introducing concepts without deep dives."
"The RL module was too short for such a complex topic, felt like just an overview."
"Could benefit from more time spent on optimization techniques or specific use cases."
"Moves quickly from one topic to the next, requires effort to keep up."
Requires a strong existing background.
"This course moves quite fast if you don't have a solid deep learning background."
"Make sure you are comfortable with Python and basic TensorFlow/Keras before starting."
"Some parts felt rushed, assuming prior knowledge of certain algorithms or concepts."
"Not recommended for absolute beginners in deep learning."
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 with TensorFlow and Keras with these
activities:
Review Linear Algebra Fundamentals
Show steps
Solidify your understanding of linear algebra concepts, which are foundational for understanding many deep learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review matrix operations such as addition, multiplication, and transposition.
-
Study vector spaces, linear independence, and basis vectors.
-
Practice solving systems of linear equations.
Review 'Deep Learning' by Goodfellow et al.
Show steps
Gain a deeper understanding of the theoretical underpinnings of deep learning models.
View
Deep Learning
on Amazon
Show steps
-
Read the chapters relevant to the current module's topics.
-
Take notes on key concepts and equations.
-
Work through the examples provided in the book.
Implement CNNs on CIFAR-10
Show steps
Reinforce your understanding of convolutional neural networks by implementing them on a standard dataset.
Show steps
-
Download the CIFAR-10 dataset using Keras.
-
Build a CNN model using Keras, experimenting with different architectures.
-
Train the model and evaluate its performance.
-
Optimize the model by tuning hyperparameters.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Reinforcement Learning: An Introduction' by Sutton and Barto
Show steps
Deepen your understanding of reinforcement learning concepts and algorithms.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Read the chapters on Q-learning and Deep Q-Networks (DQNs).
-
Work through the examples and exercises provided in the book.
-
Implement the algorithms in Keras and TensorFlow.
Write a blog post on Transformers
Show steps
Solidify your knowledge of Transformers by explaining the concepts in your own words.
Show steps
-
Research the architecture and applications of Transformers.
-
Outline the key concepts you want to cover in your blog post.
-
Write the blog post, including code examples and visualizations.
-
Publish the blog post on a platform like Medium or your personal website.
Build a Generative Model for Image Synthesis
Show steps
Apply your knowledge of generative models by building a GAN or diffusion model to generate images.
Show steps
-
Choose a dataset of images to train your model on.
-
Implement a GAN or diffusion model using Keras and TensorFlow.
-
Train the model and evaluate the quality of the generated images.
-
Experiment with different architectures and hyperparameters to improve the results.
Contribute to a TensorFlow or Keras project
Show steps
Gain practical experience and contribute to the deep learning community by contributing to an open-source project.
Show steps
-
Find a TensorFlow or Keras project on GitHub.
-
Identify an issue or feature you can contribute to.
-
Submit a pull request with your changes.
-
Respond to feedback from the project maintainers.
Review Linear Algebra Fundamentals
Show steps
Solidify your understanding of linear algebra concepts, which are foundational for understanding many deep learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review matrix operations such as addition, multiplication, and transposition.
- Study vector spaces, linear independence, and basis vectors.
- Practice solving systems of linear equations.
Review 'Deep Learning' by Goodfellow et al.
Show steps
Gain a deeper understanding of the theoretical underpinnings of deep learning models.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read the chapters relevant to the current module's topics.
- Take notes on key concepts and equations.
- Work through the examples provided in the book.
Implement CNNs on CIFAR-10
Show steps
Reinforce your understanding of convolutional neural networks by implementing them on a standard dataset.
Show steps
Show steps
Show steps
- Download the CIFAR-10 dataset using Keras.
- Build a CNN model using Keras, experimenting with different architectures.
- Train the model and evaluate its performance.
- Optimize the model by tuning hyperparameters.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Read 'Reinforcement Learning: An Introduction' by Sutton and Barto
Show steps
Deepen your understanding of reinforcement learning concepts and algorithms.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on Q-learning and Deep Q-Networks (DQNs).
- Work through the examples and exercises provided in the book.
- Implement the algorithms in Keras and TensorFlow.
Write a blog post on Transformers
Show steps
Solidify your knowledge of Transformers by explaining the concepts in your own words.
Show steps
Show steps
Show steps
- Research the architecture and applications of Transformers.
- Outline the key concepts you want to cover in your blog post.
- Write the blog post, including code examples and visualizations.
- Publish the blog post on a platform like Medium or your personal website.
Build a Generative Model for Image Synthesis
Show steps
Apply your knowledge of generative models by building a GAN or diffusion model to generate images.
Show steps
Show steps
Show steps
- Choose a dataset of images to train your model on.
- Implement a GAN or diffusion model using Keras and TensorFlow.
- Train the model and evaluate the quality of the generated images.
- Experiment with different architectures and hyperparameters to improve the results.
Contribute to a TensorFlow or Keras project
Show steps
Gain practical experience and contribute to the deep learning community by contributing to an open-source project.
Show steps
Show steps
Show steps
- Find a TensorFlow or Keras project on GitHub.
- Identify an issue or feature you can contribute to.
- Submit a pull request with your changes.
- Respond to feedback from the project maintainers.
Career center
Learners who complete Deep Learning with TensorFlow and Keras will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A machine learning engineer develops and implements machine learning algorithms and systems. This course helps machine learning engineers by providing them with advanced Keras and TensorFlow techniques. You'll explore building advanced CNNs, developing Transformer-based models for text generation, and creating custom layers. This course offers hands-on experience that is directly applicable to the responsibilities of a machine learning engineer. You will also master Keras techniques for model development by creating custom training loops and optimizing model performance.
Deep Learning Engineer
Deep Learning Engineer
A deep learning engineer specializes in designing and implementing deep neural networks. This course is directly relevant, providing training in advanced Keras and TensorFlow techniques crucial for deep learning. You'll gain hands-on experience with convolutional neural networks, Transformers, autoencoders, and GANs. You will also learn many techniques to build and optimize machine learning models. The course emphasis on Keras's functional API and custom layers will help you as a deep learning engineer to tackle complex, real-world challenges.
Natural Language Processing Engineer
Natural Language Processing Engineer
A natural language processing engineer builds models that process and generate human language. This course is helpful through coverage of advanced Transformers using Keras for sequential data tasks, including time series prediction. You'll gain hands-on experience developing Transformer-based models for text generation. A natural language processing engineer would greatly benefit from this course because it also explores how to utilize TensorFlow to manage sequential data effectively.
Computer Vision Engineer
Computer Vision Engineer
A computer vision engineer works on developing algorithms for image and video analysis. This course is particularly valuable, as it covers advanced convolutional neural networks (CNNs) using Keras. You'll learn about data augmentation, transfer learning with pre-trained models, and TensorFlow for image processing. A computer vision engineer should take this course, enabling them to solve complex computer vision tasks effectively. You will also learn to implement transpose convolution, which will be very helpful as well.
Machine Learning Consultant
Machine Learning Consultant
A machine learning consultant advises organizations on how to implement machine learning solutions. This course benefits machine learning consultants by providing practical skills in building and optimizing models using Keras and TensorFlow 2.x. You'll gain expertise in computer vision, NLP, reinforcement learning, and generative modeling. The knowledge gained in this course directly supports a machine learning consultant's ability to advise clients on complex AI challenges.
Artificial Intelligence Specialist
Artificial Intelligence Specialist
An artificial intelligence specialist focuses on researching, designing, and developing AI solutions. This course helps artificial intelligence specialists by providing practical skills in deep learning, reinforcement learning, and generative models. You'll learn to build and optimize models using Keras and TensorFlow 2.x, which helps with complex AI challenges. The deep Q-networks and reinforcement learning concepts covered can be directly applied to AI projects. You will also gain hands-on experience with computer vision and NLP.
AI Research Scientist
AI Research Scientist
An AI research scientist conducts research to advance the field of artificial intelligence. This course can benefit AI research scientists with coverage of advanced Keras functionalities, including custom layers and models. You'll explore unsupervised learning with Keras, build autoencoders, experiment with diffusion models, and develop generative adversarial networks (GANs). You will also expand your expertise in generative modeling techniques, and create custom training loops and optimizing model performance.
Quantitative Analyst
Quantitative Analyst
A quantitative analyst, often called a quant, uses mathematical and statistical methods to solve financial problems. This course helps quants through coverage of Transformers in Keras for sequential data tasks, including time series prediction. You'll develop Transformer-based models and use TensorFlow to manage sequential data effectively. This course can greatly assist a quantitative analyst, because the Transformers will be helpful for things like predicting stock prices.
Machine Learning Operations Engineer
Machine Learning Operations Engineer
A Machine Learning Operations Engineer, or MLOps Engineer, manages the end-to-end machine learning lifecycle, from model development to deployment and monitoring. This course equips machine learning operation engineers with skills in optimizing model performance using Keras Tuner and TensorFlow. You'll explore custom training loops and enhanced model optimization, valuable for streamlining machine learning workflows. You will also learn to create custom layers and models.
Research Engineer
Research Engineer
A research engineer designs and tests new technologies, often working on cutting-edge projects. This course is helpful to research engineers by covering reinforcement learning and its applications in Keras. You'll implement Q-Learning algorithms and develop deep Q-networks (DQNs) to tackle advanced reinforcement learning tasks. You will also explore hyperparameter tuning using Keras Tuner and leverage TensorFlow for enhanced model optimization and custom training workflows.
Data Scientist
Data Scientist
A data scientist analyzes and interprets complex data to drive business decisions. This course can help data scientists by providing skills in deep learning, generative models, and sequential data handling. You'll learn to build and optimize models using Keras and TensorFlow 2.x, tackling challenges in computer vision, NLP, and more. This course may be particularly helpful in expanding a data scientist's toolkit with advanced modeling techniques.
Robotics Engineer
Robotics Engineer
A robotics engineer designs, builds, and programs robots. This course can be helpful as it covers reinforcement learning and its applications in Keras. You'll implement Q-Learning algorithms and develop deep Q-networks (DQNs) to tackle advanced reinforcement learning tasks. For a robotics engineer, this course offers practical experience with a powerful AI technique that can be applied to robot control and decision-making. You will also gain hands-on experience with AI challenges.
Software Developer
Software Developer
A software developer designs and develops software applications. This course may be useful for software developers interested in integrating machine learning into their projects. You'll gain knowledge and skills to build and optimize advanced models using Keras and TensorFlow 2.x. This course can provide software developers with practical experience in AI techniques that can enhance their software applications. You will also learn to create custom layers and models in Keras.
Data Analyst
Data Analyst
A data analyst examines and interprets data to identify trends and insights. This course may be helpful for data analysts looking to enhance their skills with machine learning techniques. You'll explore Keras and TensorFlow 2.x, gaining experience in building and optimizing models. This course can broaden a data analyst's capabilities by enabling them to apply machine learning to data analysis tasks. You will also develop advanced convolutional neural networks.
Cloud Architect
Cloud Architect
A cloud architect designs and manages cloud computing infrastructure. This course may be helpful for cloud architects who need to deploy and manage machine learning models in the cloud. You'll gain knowledge of Keras and TensorFlow 2.x, which can aid in optimizing cloud-based AI solutions. This course can provide a cloud architect with a deeper understanding of the requirements for deploying machine learning applications. You will also learn to create custom layers and models in Keras.
For more career information including salaries, visit:
OpenCourser.com/course/l7tnxk/deep
Reading list
We've selected two 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 with TensorFlow and Keras.
Save
Provides a comprehensive overview of deep learning techniques. It covers the theoretical foundations and practical implementations of various deep learning models. It valuable resource for understanding the underlying principles of the algorithms used in this course. It is often used as a textbook in university-level deep learning courses.
Comprehensive introduction to reinforcement learning. It covers the theoretical foundations and algorithms used in reinforcement learning. It valuable resource for understanding the concepts covered in the final module of this course. It is widely used as a textbook for reinforcement learning courses.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l7tnxk/deep
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Rating
4.4
Effort
2 to 4 hours per week for 5 weeks
Level
Intermediate
Via
edX
Institution
IBM
Instructors
Saeed Aghabozorgi
Romeo Kienzler
Samaya Madhavan
Language
English
Transcript
English
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