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.
Deep Learning with Keras and Tensorflow
Deep Learning with Keras and Tensorflow
This course is part of IBM AI Engineering Professional Certificate
Instructors: Samaya Madhavan
Sponsored by University of Texas at Austin
32,711 already enrolled
Gain insight into a topic and learn the fundamentals.
4.4
(863 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 23 hours
Learn at your own pace
93%
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
Details to know
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Assessments
18 assignments
Taught in English
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This course is part of the IBM AI Engineering Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- 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
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There are 7 modules in this course
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
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
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
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
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
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
In this module, you will implement the final project and attempt the final assessment.
What's included
1 video2 readings1 peer review2 app items2 plugins
Instructors
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4.5 (116 ratings)Offered by
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