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.
About this Course
Syllabus - What you will learn from this course
Tensor and Datasets
Linear Regression PyTorch Way
Multiple Input Output Linear Regression
Logistic Regression for Classification
Shallow Neural Networks
- 5 stars64.31%
- 4 stars23.03%
- 3 stars5.68%
- 2 stars3.86%
- 1 star3.10%
TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH
Awesome! This course gives me the basic workflow for using machine learning technique in my research! The materials in the form of Jupyter lab really help!
Excellent course, works its way through basics to fully fledged machine learning models at a good pace. A few of the examples used in the lab code throw errors, these should be rectified
SO far, this has been the best designed and most informative of the four courses that I have taken so far in the IBM AI Engineering Certification.
Excellent Course. I love the way the course was presented. There were a lot of practical and visual examples explaining each module. It is highly recommended!
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