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
This course is part of IBM AI Engineering Professional Certificate
Sponsored by Foundation for Excellence
72,396 already enrolled
(1,683 reviews)
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.
Skills you'll gain
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There are 10 modules in this course
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