Learn PyTorch with online courses and programs
What is PyTorch?
PyTorch is an open-source machine learning framework developed and maintained by Facebook's AI Research (FAIR) lab. It is designed to provide a flexible and efficient platform for building and training deep learning models. PyTorch is written in Python and offers a dynamic computational graph, which allows for more intuitive and flexible model development compared to static graph frameworks.
Features of PyTorch include:
Neural network library
Dynamic computation graph
PyTorch has gained adoption in the deep learning community due to its ease of use, flexibility, and powerful capabilities. It has been used for research, education, and production-level applications in industries ranging from academia and healthcare to self-driving cars and natural language processing.
Browse online PyTorch courses New
PyTorch course curriculum
PyTorch is one of many tools that might be covered in a machine learning and deep learning course. An introductory deep learning course that focuses on PyTorch might cover some of the following topics, for example:
Tensors, computational graphs, and the PyTorch API
Building neural networks
Training and evaluating deep learning models
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Learning about how to use PyTorch can supplement your knowledge of machine learning and data science. edX offers a variety of educational opportunities for learners interested in studying these topics, as well as a host of other disciplines. A boot camp can provide flexible, hands-on learning for those who want to upskill quickly, while executive education courses are designed for busy professionals. You can also pursue a more comprehensive curriculum in a bachelor’s degree or, for more advanced learners, a master’s degree. Find the right learning path for you.
Explore jobs related to PyTorch
Deep learning expertise is necessary across industries, presenting diverse opportunities for individuals skilled in leveraging PyTorch for artificial intelligence applications. Some examples of such jobs could include:
Deep learning engineer: Focuses on designing, implementing, and optimizing deep learning models using frameworks like PyTorch. They work on tasks such as computer vision, natural language processing, speech recognition, and recommendation systems.
Data scientist: Applies deep learning techniques to analyze and extract insights from large and complex datasets. They work on tasks such as data preprocessing, feature engineering, model training, and evaluation to solve business problems.
Computer vision engineer: Leverages PyTorch to develop and deploy deep learning models for tasks such as object detection, image segmentation, facial recognition, and autonomous driving.
Natural Language Processing (NLP) engineer: Utilizes PyTorch to build models for tasks like text classification, sentiment analysis, language translation, named entity recognition, and chatbot development.
Each of these roles will have different education and skills requirements. For example, you may be able to build relevant skills in a coding boot camp. However, some employers may seek candidates with a degree in computer science, depending on the role. Before deciding on a specific learning path, research the positions you hope to pursue and align your coursework with your career goals.