IBM
Introduction to Deep Learning & Neural Networks with Keras
IBM

Introduction to Deep Learning & Neural Networks with Keras

Alex Aklson

Instructor: Alex Aklson

Sponsored by Southeastern University

61,596 already enrolled

Gain insight into a topic and learn the fundamentals.
4.7

(1,642 reviews)

Intermediate level
Some related experience required
Flexible schedule
Approx. 8 hours
Learn at your own pace
93%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.7

(1,642 reviews)

Intermediate level
Some related experience required
Flexible schedule
Approx. 8 hours
Learn at your own pace
93%
Most learners liked this course

Details to know

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Assessments

4 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 also learn about neural networks and how most of the deep learning algorithms are inspired by the way our brain functions and the neurons process data. Finally, you will learn about how neural networks feed data forward through the network.

What's included

4 videos1 reading1 assignment1 app item

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. Futhermore, you will learn about the vanishing gradient problem. Finally, you will learn about activation functions.

What's included

4 videos1 assignment

In this module, you will learn about the diifferent deep learning libraries namely, Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.

What's included

3 videos1 assignment2 app items

In this module, you will learn about the difference between the 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

4 videos1 assignment1 app item

In this module, you will conclude the course by working on a final assignment where you will use the Keras library to build a regression model and experiment with the depth and the width of the model.

What's included

1 video1 peer review

Instructor

Instructor ratings
4.7 (337 ratings)
Alex Aklson
IBM
22 Courses1,155,374 learners

Offered by

IBM

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