Deep Learning

Through our guided lectures and labs, you'll first learn Neural Networks, and an overview of Deep Learning, then get hands-on experience using TensorFlow library to apply deep learning on different data types to solve real world problems.

In this learning path, you will be able to learn the basic concepts of Deep Leaning and TensorFlow. Then, you will get hands-on experience in solving problems using Deep Learning. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

Courses

Deep Learning Fundamentals

Effort: 5 Hours
Level: Intermediate
Available in: English
About the course

The further one dives into the ocean, the more unfamiliar the territory can become. Deep learning, at the surface might appear to share similarities. This course is designed to get you hooked on the nets and coders all while keeping the school together.

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Deep Learning with TensorFlow

Effort: 10 Hours
Level: Advanced
Available in: English
TensorFlow Course
About the course

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this TensorFlow course you'll use Google's library to apply deep learning to different data types in order to solve real world problems.

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Accelerating Deep Learning with GPU

Effort: 5 hours
Level: Advanced
About the course

Training complex deep learning models with large datasets take long time. In this course you learn how to use accelerated hardware to overcome the scalability problem in deep learning.

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Complete Deep Learning Learning path

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