PyTorch is an open-source deep learning framework created by Facebook’s AI Research lab. It is used to develop and train deep learning mechanisms such as neural networks. Some of the world’s biggest tech companies, including Google, Microsoft, and Apple, use it. If you’re looking to get started with PyTorch, then you’ve come to the right place. We’ll be taking a look at the 10 best PyTorch courses available online.
Everyone interested in learning more about PyTorch, from beginners to seasoned professionals, would benefit greatly from taking one of these courses. No matter what your budget is, you’ll be able to locate the course that meets your needs because we’ll cover both free and paid courses.
So, if you’re ready to take your PyTorch knowledge to the next level, let’s dive in and explore the 10 best PyTorch courses out there.
This course is designed to equip learners with the skills to implement Machine and Deep Learning applications with PyTorch. It provides an overview of the PyTorch framework for deep learning and computer vision applications. Learners will get hands-on experience building Neural Networks from scratch. They’ll learn to build complex models through the applied theme of Advanced Imagery.
Duration: 14 hours and 14 minutes
This PyTorch course provides an introduction to the theoretical underpinnings of deep learning algorithms and how they are implemented with PyTorch. It covers how to use PyTorch to implement common machine-learning algorithms for image classification. By the end of the course, you will have a strong understanding of using PyTorch. You’ll be able to create and train deep learning models.
Duration: 6 hours and 18 minutes with 52 lectures.
Certificate: Certificate of completion
3. Foundations of PyTorch [Pluralsight]
This course gives students a foundational understanding of PyTorch. Students will learn about neurons and neural networks and how activation functions. Students will also explore how to build dynamic computation graphs in PyTorch and contrast that with the approaches used in TensorFlow. By the end of this course, students will have the skills to move on to building deep learning models in PyTorch.
Duration: 2 Hours and 51 Minutes
4. Deep Neural Networks with PyTorch [Coursera]
This Pytorch course teaches students how to deploy deep learning models using PyTorch. It begins by introducing PyTorch’s tensors and the Automatic Differentiation package, then covers models such as Linear Regression, Logistic/Softmax regression, and Feedforward Deep Neural Networks. In addition, the course also deep dives into the role of different normalization, dropout layers, and different activation functions. And this isn’t it; you can also explore transfer learning and convolutional Neural Networks.
Duration: 30 Hours
5. Make Your First GAN Using PyTorch [educative]
This is an ideal introduction to (GANs) and provides a tutorial on building GANs with PyTorch. Students will learn to build a Generative adversarial network and understand their concepts. In the first section, you will gain an understanding of neural networks by building a simple image classifier. In the second section, you will explore the concept of adversarial training and build progressively complex GANs.
Duration: The course is expected to take about 13 hours to complete.
This course offers an introduction to the fundamentals of deep learning and neural networks using Python and PyTorch. Students will learn the basics of deep learning and how to build deep neural networks. They’ll also learn to build deep learning pipelines for different tasks and applications. This course is suitable for students with no prior knowledge of deep learning. At the end of the course, students will be able to build deep learning models, understand their internal workings, and apply them to real-world tasks.
Duration: This course lasts for 6 weeks, with 2-4 hours of weekly study.
7. Intro to Deep Learning with PyTorch [Udacity]
This PyTorch course is a comprehensive introduction to the field of Deep Learning and its applications. In this course, you will learn the basics of deep learning and build your own deep neural networks. With practical exercises and projects, you will gain experience and learn to implement state-of-the-art AI applications such as style transfer and text generation.
Duration: The course duration is approx. two months.
8. Deep Learning with PyTorch: Image Segmentation [Coursera]
Image Segmentation is aimed at providing the fundamentals of Image Segmentation. This course covers the major techniques used in Image Segmentation, such as Understanding the Segmentation Dataset and Writing a custom dataset class for the Image-mask dataset. Teaches how to apply segmentation augmentation to images and masks. It also includes loading a pre-trained convolutional neural network for segmentation.
Duration: This course is 2 Hours.
You’ll learn to use NumPy to format data into arrays to manipulate and clean data with pandas. The best part is that you get a quick rundown on the basic principles of machine learning. Explore more on image classification by using PyTorch Deep Learning Library for the purpose. Get practical training by using recurrent neural networks that are for the sequence time data series and create Deep Learning models to work with tabular data.
Duration: It takes around 17 hours to complete
10. Practical Deep Learning with PyTorch [Udemy]
Students who take this course will better grasp deep learning. Deep learning basics, neural networks, supervised and unsupervised learning, and other subjects are covered. The instructor also offers advice on using deep learning models in real-world applications. Both beginners and experts can benefit from the course, which is designed for students of all skill levels.
Duration: 6 hours and 26 minutes
PyTorch is a potent and widely used deep learning framework that provides developers with a number of advantages. With so many excellent PyTorch courses available online, there’s no excuse to start your journey to mastering PyTorch!
Just consider this thought-provoking question what if PyTorch can address the most critical issues facing the globe? Might it be used, for instance, to improve climate models or contribute to forecasting and to prevent natural disasters? The possibilities are endless, and PyTorch will provide you with the necessary capabilities to take on even the most challenging tasks. So why not explore the PyTorch courses available today and build a brighter tomorrow?
Frequently Asked Questions
What Is Py Torch?
PyTorch is an open-source deep-learning framework developed by Facebook. It builds and trains deep learning models such as neural networks.
What Are the Benefits of Py Torch?
PyTorch offers various benefits, such as dynamic computational graphs, ease of use, flexibility, and strong community support. It also has a Python-based interface, making it easy to learn and use.
What Kind of Applications Can Be Developed Using Py Torch?
PyTorch can be used to develop and train a variety of deep learning models, such as image and speech recognition, natural language processing, and recommender systems.
Do I Need to Know Python to Use Py Torch?
Yes, Python is a prerequisite for using PyTorch, as it is the primary language used for building and training deep learning models.
Is Py Torch Difficult to Learn?
PyTorch can be relatively easy to learn, especially for those with prior experience in Python programming and deep learning. However, it may require some time and effort to fully master its advanced features and functionalities.