Deep Learning with Keras
Master deep learning fundamentals and build neural networks using Keras in this comprehensive course for aspiring AI practitioners.
Master deep learning fundamentals and build neural networks using Keras in this comprehensive course for aspiring AI practitioners.
This intermediate-level course provides a solid foundation in deep learning fundamentals and practical implementation using Keras. Students learn about neural networks, their learning mechanisms, and various deep learning models. The course covers essential concepts like gradient descent, backpropagation, and activation functions, while providing hands-on experience with Keras for building regression and classification models. Special attention is given to both supervised learning (CNNs, RNNs) and unsupervised learning (autoencoders). The curriculum emphasizes practical application while building fundamental theoretical understanding, preparing students for careers in AI and deep learning.
4.4
(25 ratings)
49,264 already enrolled
Instructors:
English
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What you'll learn
Understand neural networks and their learning mechanisms
Master deep learning fundamentals and key concepts
Build regression and classification models using Keras
Implement supervised and unsupervised deep learning models
Apply convolutional and recurrent neural networks
Develop practical solutions for real-world AI problems
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
This comprehensive course introduces students to deep learning fundamentals and practical implementation using Keras. The curriculum covers neural network basics, deep learning models, and hands-on model building. Students learn about gradient descent, backpropagation, activation functions, and various neural network architectures. The course emphasizes both theoretical understanding and practical application, featuring supervised and unsupervised learning models, with special focus on real-world implementation using the Keras library.
Introduction to Deep Learning
Module 1
Artificial Neural Networks
Module 2
Deep Learning Libraries
Module 3
Deep Learning Models
Module 4
Fee Structure
Instructor
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.
Testimonials
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4.4 course rating
25 ratings
Frequently asked questions
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