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.
Introduction to Deep Learning & Neural Networks with Keras
Introduction to Deep Learning & Neural Networks with Keras
This course is part of multiple programs.
Instructor: Alex Aklson
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4.7
(1,796 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 9 hours
Learn at your own pace
93%
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Assessments
8 assignments
Taught in English
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There are 5 modules in this course
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will learn about neural networks and how most deep learning algorithms are inspired by how our brain functions and the neurons process data. You will also learn about how neural networks feed data forward through the network.
What's included
4 videos2 readings2 assignments1 app item1 plugin
In this module you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Furthermore, you will learn about the vanishing gradient problem and how activation functions are used in artificial neural networks.
What's included
4 videos1 reading2 assignments2 app items
In this module, you will learn about the different deep learning libraries: Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.
What's included
3 videos1 reading2 assignments2 app items
In this module, you will learn the difference between shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.
What's included
6 videos1 reading2 assignments2 app items
In this module, you will conclude the course by working on a final assignment where, you will classify aircraft damage using a pre-trained VGG16 model and generate captions using a Transformer-based pretrained model.
What's included
1 video2 readings2 app items
Instructor
Instructor ratings
4.7 (383 ratings)Offered by
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University of Colorado Boulder
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4.7
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Showing 3 of 1796
AM
5
Reviewed on Jun 24, 2020
Good course. It is a very direct approach. It is a basic introduction to keras. Doing the labs is recommended, and also previous knowledge about machine learning is encouraged.
AR
4
Reviewed on Jul 10, 2024
The course is quite complex for a person who does not have knowledge of algebra, statistics and calculus, the final project was good because it was challenging.
BJ
4
Reviewed on Oct 9, 2019
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
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