Deep Learning with Keras and Tensorflow

Deep Learning with Keras and Tensorflow

This course is part of multiple programs.

Instructors: Samaya Madhavan

Access provided by Fractal

54,785 already enrolled

7 modules

Gain insight into a topic and learn the fundamentals.

1,023 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

92%

Most learners liked this course

7 modules

Gain insight into a topic and learn the fundamentals.

1,023 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

92%

Most learners liked this course

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

18 assignments¹

AI Graded see disclaimer

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is available as part of

When you enroll in this course, you'll also be asked to select a specific program.

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

Graded Quiz: Advanced Keras Functionalities 30 minutes
Practice Quiz: Advanced Keras Functional API 10 minutes
Practice Quiz: Custom Layers with Keras 10 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

Graded Quiz: Advanced CNNs in Keras 30 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

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

Graded Quiz: Transformers in Keras 30 minutes
Practice Quiz: Transformers in Keras 10 minutes
Practice Quiz: Advanced Transformers and Sequential Data using TensorFlow 10 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

Graded Quiz: Unsupervised Learning and Generative Models in Keras 30 minutes
Practice Quiz: Unsupervised Learning, Autoencoders, and Diffusion Models 10 minutes
Practice Quiz: GANs and TensorFlow 10 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

Advanced Keras Techniques 30 minutes
Practice Quiz: Advanced Keras Techniques and Custom Training Loops 10 minutes
Practice Quiz: Hyperparameter and Model Optimization 15 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

Graded Quiz: Introduction to Reinforcement Learning with Keras 30 minutes
Practice Quiz: Reinforcement Learning, Q-Learning, Q-Networks (DQNs) 10 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

Practice Project: Fruit Classification Using Transfer Learning 60 minutes
Final Project: Classify Waste Products Using Transfer Learning 60 minutes
Option 1: AI Graded - Final Project: Submission and Evaluation 5 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

Instructor ratings

(150 ratings)

Samaya Madhavan

IBM

1 Course 54,785 learners

Ricky Shi

IBM

2 Courses 62,072 learners

Alex Aklson

IBM

21 Courses 1,403,235 learners

Offered by

IBM

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
65.10%
4 stars
21.05%
3 stars
7.69%
2 stars
2.92%
1 star
3.21%

Showing 3 of 1023

Reviewed on May 16, 2023

very well-constructed course for deep learning students. Really enjoyed. Many thanks for IBM

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 Mar 4, 2021

This course is the best out of all courses in the specialization, the pace of the speaker was perfect.

View more reviews