Introduction to Deep Learning & Neural Networks with Keras

( 17 Reviews )

Created by: Alex Aklson

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Overall Score : 92 / 100

Course Description

Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.After completing this course, learners will be able to:-describe what a neural network is, what a deep learning model is, and the difference between them.-demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines.-demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks.-build deep learning models and networks using the Keras library.

Time: 10h

Instructor Details

Alex Aklson

4.6 Rating
17 Reviews

Alex Aklson, Ph.D., is a data scientist in the Digital Business Group at IBM Canada. Alex has been intensively involved in many exciting data science projects such as designing a smart system that could detect the onset of dementia in older adults using longitudinal trajectories of walking speed and home activity. Before joining IBM, Alex worked as a data scientist at Datascope Analytics, a data science consulting firm in Chicago, IL, where he designed solutions and products using a human-centred, data-driven approach. Alex received his Ph.D. in medical Engineering from the University of Toronto.

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Reviews

4.6

17 total reviews

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By Suresh C on 5-Nov-19

These course is just for Introduction in Neural Network and Keras

By Simone P on 3-Oct-19

It seems more a review of concepts that you need to have already have studied on other courses/books than a real introduction.

By Benhur O J on 10-Oct-19

Good practical examples for ANN. It could be improved the theoretical part and compare better the architecture of the networks with the algorithms and code for Keras

By mallesh on 8-Oct-19