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Updated in [February 21st, 2023]

What does this course tell?
(Please note that the following overview content is from the original platform)

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

NOTE: In order to be successful in completing this course, please ensure you are familiar with PyTorch Basics and have practical knowledge to apply it to Machine Learning. If you do not have this pre-requiste knowledge, it is highly recommended you complete the PyTorch Basics for Machine Learning course prior to starting this course.

This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.

In the first course, you learned the basics of PyTorch; in this course, you will learn how to build deep neural networks in PyTorch. Also, you will learn how to train these models using state of the art methods. You will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. This will be followed by an in-depth introduction on how to construct Feed-forward neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons.

You will then learn how to build and train deep neural networks—learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization. We will then focus on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). You will finally learn about dimensionality reduction and autoencoders. Including principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications.

Finally, you will test your skills in a final project.
What can you get from this course?
We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
(Kindly be aware that our content is optimized by AI tools while also undergoing moderation carefully from our editorial staff.)
What skills and knowledge will you acquire during this course?
This course will provide learners with the skills and knowledge to build and train a multiclass linear classifier in PyTorch, construct and train feed-forward neural networks, apply methods such as dropout, initialization, different types of optimizers and batch normalization to deep neural networks, train models on a GPU and use Transfer Learning (pre-trained models) with convolutional neural networks, and understand principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications. Learners will also have the opportunity to test their skills in a final project.

How does this course contribute to professional growth?
This course provides learners with the opportunity to gain a comprehensive understanding of deep learning with Python and PyTorch. Through the course, learners will gain an understanding of multiclass classification, feed-forward neural networks, deep neural networks, convolutional neural networks, dimensionality reduction and autoencoders. Additionally, learners will have the opportunity to apply their knowledge in a final project. This course will provide learners with the skills and knowledge necessary to pursue professional growth in the field of deep learning.

Is this course suitable for preparing further education?
Deep Learning with Python and PyTorch is a suitable course for preparing further education. It covers a variety of topics, such as multiclass classification, feed-forward neural networks, deep neural networks, convolutional neural networks, dimensionality reduction and autoencoders. Learners will gain an understanding of how to build and train models, adjust hyperparameters, use transfer learning, and apply autoencoders. The course also includes a final project to test the skills learned. Therefore, this course is suitable for preparing further education.