Deep Learning with Keras and Tensorflow
Keep adding new skills with 10,000+ programs for $239 (usually $399). Save now.
Deep Learning with Keras and Tensorflow
This course is part of multiple programs.
59,580 already enrolled
Included with
Ask Coursera
1,034 reviews
Recommended experience
1,034 reviews
Recommended experience
What you'll learn
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
Skills you'll gain
Details to know
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from IBM
There are 7 modules in this course
Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In addition, Keras, a high-level neural networks API written in Python, has become an essential part of TensorFlow, making deep learning accessible and straightforward. Mastering these techniques will open many opportunities in research and industry.
You will learn to create custom layers and models in Keras and integrate Keras with TensorFlow 2.x for enhanced functionality. You will develop advanced convolutional neural networks (CNNs) using Keras. You will also build transformer models for sequential data and time series using TensorFlow with Keras. The course also covers the principles of unsupervised learning in Keras and TensorFlow for model optimization and custom training loops. Finally, you will develop and train deep Q-networks (DQNs) with Keras for reinforcement learning tasks (an overview of Generative Modeling and Reinforcement Learning is provided). You will be able to practice the concepts learned using hands-on labs in each lesson. A culminating final project in the last module will provide you an opportunity to apply your knowledge to build a Classification Model using transfer learning. This course is suitable for all aspiring AI engineers who want to learn TensorFlow and Keras. It requires a working knowledge of Python programming and basic mathematical concepts such as gradients and matrices, as well as fundamentals of Deep Learning using Keras.
This module provides an overview of Keras advanced features. It will cover Keras functional API for complex model creation. It also includes the creation of custom layers and models in Keras. Then the module describes the integration of Keras with TensorFlow 2.x for enhanced functionality. You will apply your learnings in labs and test your concepts in quizzes.
What's included
5 videos2 readings3 assignments2 app items1 discussion prompt2 plugins
5 videos•Total 21 minutes
- Course Introduction•3 minutes
- Introduction to Advanced Keras •4 minutes
- Keras Functional API and Subclassing API•6 minutes
- Creating Custom Layers in Keras •3 minutes
- Overview of TensorFlow 2.x •6 minutes
2 readings•Total 9 minutes
- Course Overview•4 minutes
- Summary and Highlights: Advanced Keras Functionalities •5 minutes
3 assignments•Total 50 minutes
- Practice Quiz: Advanced Keras Functional API •10 minutes
- Practice Quiz: Custom Layers with Keras•10 minutes
- Graded Quiz: Advanced Keras Functionalities •30 minutes
2 app items•Total 60 minutes
- Lab: Implementing the Functional API in Keras•30 minutes
- Lab: Creating Custom Layers and Models•30 minutes
1 discussion prompt•Total 10 minutes
- [Optional] Meet and Greet •10 minutes
2 plugins•Total 16 minutes
- Helpful Tips for Course Completion•1 minute
- Glossary: Advanced Keras Functionalities •15 minutes
In this module, you will learn to develop advanced convolutional neural networks (CNNs) using Keras. You will learn data augmentation techniques with Keras. In addition, you will implement transfer learning with Keras and leverage pre-trained models. Finally, you will learn how to use TensorFlow for enhancing image processing capabilities. You will apply your learnings in labs and test your concepts in quizzes.
What's included
6 videos1 reading4 assignments3 app items1 discussion prompt2 plugins
6 videos•Total 27 minutes
- Advanced CNNs in Keras •5 minutes
- Data Augmentation Techniques •3 minutes
- Transfer Learning in Keras •7 minutes
- Using Pre-trained Models •5 minutes
- TensorFlow for Image Processing •3 minutes
- Introducing Transpose Convolution •4 minutes
1 reading•Total 1 minute
- Summary and Highlights: Advanced CNNs in Keras •1 minute
4 assignments•Total 60 minutes
- Practice Quiz: Advanced CNNs and Data Augmentation•10 minutes
- Practice Quiz: Transfer Learning on Pre-trained Models and Image Processing•10 minutes
- Practice Quiz: Introducing Transpose Convolution •10 minutes
- Graded Quiz: Advanced CNNs in Keras•30 minutes
3 app items•Total 120 minutes
- Advanced Data Augmentation with Keras•30 minutes
- Lab: Transfer Learning Implementation•30 minutes
- Lab: Practical Application of Transpose Convolution•60 minutes
1 discussion prompt•Total 10 minutes
- [Optional] Discussion Prompt: Data Augmentation and Transfer Learning•10 minutes
2 plugins•Total 20 minutes
- Reading: Tips for Transfer Learning Implementation•5 minutes
- Glossary: Advanced CNNs in Keras •15 minutes
This module covers building and training advanced Transformers using Keras. You will further develop Transformer models for sequential data and time series using TensorFlow with Keras. In addition, you will learn to implement advanced Transformer techniques for text generation. You will apply your learnings in labs and test your concepts in quizzes.
What's included
5 videos1 reading3 assignments2 app items1 discussion prompt1 plugin
5 videos•Total 21 minutes
- Introduction to Transformers in Keras •5 minutes
- Building Transformers for Sequential Data •3 minutes
- Advanced Transformer Applications •4 minutes
- Transformers for Time Series Prediction •4 minutes
- TensorFlow for Sequential Data •4 minutes
1 reading•Total 3 minutes
- Summary and Highlights: Transformers in Keras •3 minutes
3 assignments•Total 50 minutes
- Practice Quiz: Transformers in Keras •10 minutes
- Practice Quiz: Advanced Transformers and Sequential Data using TensorFlow•10 minutes
- Graded Quiz: Transformers in Keras •30 minutes
2 app items•Total 90 minutes
- Lab: Building Advanced Transformers•60 minutes
- Lab: Implementing Transformers for Text Generation•30 minutes
1 discussion prompt•Total 10 minutes
- [Optional] Discussion Prompt: Transforming Sequential Data with Transformers•10 minutes
1 plugin•Total 15 minutes
- Glossary: Transformers in Keras •15 minutes
In this module, you will learn the principles of unsupervised learning in Keras. You will learn to build and train autoencoders and diffusion models. In addition, you will develop generative adversarial networks (GANs) using Keras and integrate TensorFlow for advanced unsupervised learning tasks. You will apply your learnings in labs and test your concepts in quizzes.
What's included
5 videos1 reading3 assignments3 app items1 discussion prompt1 plugin
5 videos•Total 19 minutes
- Introduction to Unsupervised Learning in Keras •5 minutes
- Building Autoencoders in Keras•4 minutes
- Diffusion Models •4 minutes
- Generative Adversarial Networks (GANs) •4 minutes
- TensorFlow for Unsupervised Learning •3 minutes
1 reading•Total 2 minutes
- Summary and Highlights: Unsupervised Learning and Generative Models in Keras •2 minutes
3 assignments•Total 50 minutes
- Practice Quiz: Unsupervised Learning, Autoencoders, and Diffusion Models •10 minutes
- Practice Quiz: GANs and TensorFlow •10 minutes
- Graded Quiz: Unsupervised Learning and Generative Models in Keras •30 minutes
3 app items•Total 135 minutes
- Lab: Building Autoencoders•60 minutes
- Lab: Implementing Diffusion Models•45 minutes
- Lab: Develop GANs using Keras•30 minutes
1 discussion prompt•Total 10 minutes
- [Optional] Exploring Autoencoders and GANs•10 minutes
1 plugin•Total 15 minutes
- Glossary: Unsupervised Learning and Generative Models in Keras •15 minutes
In this module, you will learn advanced techniques in Keras for model development. You will create custom training loops and optimize models using Keras and perform hyperparameter tuning with Keras Tuner. Finally, you will learn to use TensorFlow for model optimization and custom training loops. You will apply your learnings in labs and test your concepts in quizzes.
What's included
5 videos1 reading3 assignments2 app items1 discussion prompt1 plugin
5 videos•Total 18 minutes
- Advanced Keras Techniques •3 minutes
- Custom Training Loops in Keras •3 minutes
- Hyperparameter Tuning with Keras Tuner •4 minutes
- Model Optimization •4 minutes
- TensorFlow for Model Optimization •4 minutes
1 reading•Total 2 minutes
- Summary and Highlights: Advanced Keras Techniques •2 minutes
3 assignments•Total 55 minutes
- Practice Quiz: Advanced Keras Techniques and Custom Training Loops •10 minutes
- Practice Quiz: Hyperparameter and Model Optimization •15 minutes
- Advanced Keras Techniques•30 minutes
2 app items•Total 90 minutes
- Lab: Custom Training Loops in Keras•30 minutes
- Lab: Hyperparameter Tuning with Keras Tuner•60 minutes
1 discussion prompt•Total 10 minutes
- [Optional] Discussion Prompt: Custom Training Loops and Hyperparameter Optimization•10 minutes
1 plugin•Total 15 minutes
- Glossary: Advanced Keras Techniques•15 minutes
In this module, you will learn the fundamentals of reinforcement learning and its applications in Keras. The module also covers the Q-Learning algorithms using Keras. You will develop and train deep Q-networks (DQNs) with Keras for advanced reinforcement learning tasks. You will apply your learnings in labs and test your concepts in quizzes.
What's included
3 videos1 reading2 assignments2 app items1 discussion prompt1 plugin
3 videos•Total 21 minutes
- Reinforcement Learning (RL)•8 minutes
- Q-Learning with Keras •8 minutes
- Deep Q-Networks (DQNs) with Keras •5 minutes
1 reading•Total 1 minute
- Summary and Highlights: Introduction to Reinforcement Learning with Keras•1 minute
2 assignments•Total 40 minutes
- Practice Quiz: Reinforcement Learning, Q-Learning, Q-Networks (DQNs) •10 minutes
- Graded Quiz: Introduction to Reinforcement Learning with Keras •30 minutes
2 app items•Total 120 minutes
- Lab: Implementing Q-Learning in Keras•60 minutes
- Lab: Building a Deep Q-Network with Keras•60 minutes
1 discussion prompt•Total 10 minutes
- [Optional] Discussion Prompt: The Promise and Challenge of Reinforcement Learning•10 minutes
1 plugin•Total 5 minutes
- Glossary: Introduction to Reinforcement Learning with Keras•5 minutes
In this module, you will implement the final project and attempt the final assessment.
What's included
1 video2 readings1 peer review3 app items3 plugins
1 video•Total 7 minutes
- Course Wrap-up•7 minutes
2 readings•Total 4 minutes
- Congratulations and Next Steps•2 minutes
- Thanks from the Course Team•2 minutes
1 peer review•Total 20 minutes
- Option 2: Peer Graded - Final Project Submission and Evaluation•20 minutes
3 app items•Total 125 minutes
- Option 1: AI Graded - Final Project: Submission and Evaluation•5 minutes
- Practice Project: Fruit Classification Using Transfer Learning•60 minutes
- Final Project: Classify Waste Products Using Transfer Learning•60 minutes
3 plugins•Total 35 minutes
- Practice Project Overview: Fruit Classification Using Transfer Learning•15 minutes
- Final Project Overview•15 minutes
- Reading: Final Project Submission Guidelines and Deliverables•5 minutes
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructors
Why people choose Coursera for their career
Learner reviews
- 5 stars
65.34%
- 4 stars
20.94%
- 3 stars
7.62%
- 2 stars
2.89%
- 1 star
3.18%
Showing 3 of 1034
Reviewed on Jul 25, 2020
Nice course to introduce you to more advanced neural network algorithms, I wish the evaluations were more challenging and based on practical exercises... there is no final assignment either.
Reviewed on May 18, 2025
It is a very detailed course for those looking for learning more about Keras and Tensorflow.
Reviewed on Mar 4, 2021
This course is the best out of all courses in the specialization, the pace of the speaker was perfect.
Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
More questions
Financial aid available,
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.