The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
Deep Neural Networks with PyTorch
Deep Neural Networks with PyTorch
This course is part of IBM AI Engineering Professional Certificate
Instructor: Joseph Santarcangelo
72,874 already enrolled
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4.4
(1,683 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 31 hours
Learn at your own pace
91%
Most learners liked this course
What you'll learn
Demonstrate your comprehension of deep learning algorithims and implement them using Pytorch.
Explain and apply knowledge of Deep Neural Networks and related machine learning methods.
Describe how to use Python libraries such as PyTorch for Deep Learning applications.
Build Deep Neural Networks using PyTorch.
Details to know
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Assessments
1 quiz, 42 assignments
Taught in English
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This course is part of the IBM AI Engineering Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
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There are 10 modules in this course
What's included
6 videos5 assignments6 app items1 plugin
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4 videos2 assignments4 app items
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4 videos1 quiz4 assignments3 app items
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3 videos3 assignments2 app items
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6 videos6 assignments6 app items
What's included
6 videos6 assignments10 app items
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7 videos5 assignments6 app items1 plugin
What's included
1 peer review1 app item
Instructor
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4.5 (368 ratings)Offered by
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