Building Deep Learning Models with TensorFlow
Master deep learning with TensorFlow 2.x, from neural networks to advanced architectures like CNNs and Transformers.
Master deep learning with TensorFlow 2.x, from neural networks to advanced architectures like CNNs and Transformers.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full IBM AI Engineering Professional Certificate program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
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What you'll learn
Create custom neural network architectures with Keras
Implement advanced CNNs for computer vision tasks
Develop Transformer models for sequential data
Build unsupervised learning models and autoencoders
Master deep Q-networks for reinforcement learning
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
5 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 7 modules in this course
This comprehensive course covers advanced deep learning techniques using TensorFlow and Keras. Students learn to build and customize various neural network architectures including CNNs, RNNs, and Transformers. The curriculum includes both supervised and unsupervised learning approaches, with hands-on implementation of models for computer vision, natural language processing, and reinforcement learning tasks.
Advanced Keras Functionalities
Module 1 · 2 Hours to complete
Advanced CNNs in Keras
Module 2 · 3 Hours to complete
Transformers in Keras
Module 3 · 3 Hours to complete
Unsupervised Learning and Generative Models in Keras
Module 4 · 3 Hours to complete
Advanced Keras Techniques
Module 5 · 3 Hours to complete
Introduction to Reinforcement Learning with Keras
Module 6 · 3 Hours to complete
Final Project and Assignment
Module 7 · 3 Hours to complete
Fee Structure
Instructors
Expert in Building Deep Learning Models with TensorFlow at IBM
Jeremy Nilmeier is a Data Scientist and Developer Advocate at IBM, where he teaches the course "Building Deep Learning Models with TensorFlow." This course provides a comprehensive introduction to deep learning using TensorFlow, covering topics such as neural network architectures, optimization techniques, and best practices for model development and deployment. Participants will gain hands-on experience building and training deep learning models for various applications, including computer vision and natural language processing.
Dr. Alex Aklson: Crafting Data-Driven Solutions and Innovating Smart Health Systems at IBM
Dr. Alex Aklson is a data scientist in IBM Canada’s Digital Business Group, where he has contributed to innovative projects, including the development of a smart system to detect early signs of dementia by analyzing walking speed and home activity patterns in older adults. Prior to IBM, Alex worked at Datascope Analytics in Chicago, where he crafted data-driven solutions using a human-centered approach. He holds a Ph.D. in Biomedical Engineering from the University of Toronto.
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4.4 course rating
844 ratings
Frequently asked questions
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