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Janani Ravi
PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. Using PyTorch, you can build complex deep learning models, while still using Python...
Read more
PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. Using PyTorch, you can build complex deep learning models, while still using Python...
Read more
PyTorch is fast emerging as a popular choice for building deep learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs. Using PyTorch, you can build complex deep learning models, while still using Python-native support for debugging and visualization. In this course, Foundations of PyTorch, you will gain the ability to leverage PyTorch support for dynamic computation graphs, and contrast that with other popular frameworks such as TensorFlow. First, you will learn the internals of neurons and neural networks, and see how activation functions, affine transformations, and layers come together inside a deep learning model. Next, you will discover how such a model is trained, that is, how the best values of model parameters are estimated. You will then see how gradient descent optimization is smartly implemented to optimize this process. You will understand the different types of differentiation that could be used in this process, and how PyTorch uses Autograd to implement reverse-mode auto-differentiation. You will work with different PyTorch constructs such as Tensors, Variables, and Gradients. Finally, you will explore how to build dynamic computation graphs in PyTorch. You will round out the course by contrasting this with the approaches used in TensorFlow, another leading deep learning framework which previously offered only static computation graphs, but has recently added support for dynamic computation graphs. When you’re finished with this course, you will have the skills and knowledge to move on to building deep learning models in PyTorch and harness the power of dynamic computation graphs.
Register for this course and see more details by visiting:
OpenCourser.com/course/y48jhq/foundations
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Designed for aspiring and practicing deep learning engineers
Weakens the foundation of a static graph, which is industry standard
Instructors have proven track record in building deep learning models
Builds strong skills in constructing dynamic computation graphs, which are crucial for deep learning models
Emphasizes Python-native support for debugging and visualization, which is preferred in the industry
Uses Tensors, Variables, and Gradients, which are essential concepts for deep learning in PyTorch
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Career center
Learners who complete Foundations of PyTorch will develop knowledge and skills
that may be useful to these careers:
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers are responsible for developing and deploying deep learning models. This course may help build a foundation for Deep Learning Engineers by helping them build a foundation in the essentials of PyTorch, one of the most popular deep learning frameworks in the world. Going further, understanding the concepts taught in this course can help a Deep Learning Engineer architect complex deep learning models for any purpose.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of math, statistics, and programming to develop and implement complex financial models. This course may help build a foundation for Quantitative Analysts by helping them build a foundation in the essentials of PyTorch, one of the most popular deep learning frameworks in the world. Going further, understanding the concepts taught in this course can help a Quantitative Analyst architect complex deep learning models for any purpose.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning models. This course may help build a foundation for Machine Learning Engineers by helping them build a foundation in the essentials of PyTorch, one of the most popular deep learning frameworks in the world. Going further, understanding the concepts taught in this course can help a Machine Learning Engineer architect complex deep learning models.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers work on all aspects of machine learning, from developing new algorithms to testing and deploying new models. This course may help build a foundation for Machine Learning Researchers by teaching them the foundations of PyTorch, a powerful tool for building deep learning models. Beyond that, learning the concepts taught in this course can help a Machine Learning Researcher explore complex deep learning models for any purpose.
Software Engineer
Software Engineer
Software Engineers work on all aspects of software development, from designing and building to testing and deploying. This course may help build a foundation for Software Engineers by teaching them the foundations of PyTorch, a powerful tool for building deep learning models. Beyond that, learning the concepts taught in this course can help a Software Engineer build and deploy applications that use these models.
Research Scientist
Research Scientist
Research Scientists work on all aspects of scientific research, from developing new theories to testing and deploying new models. This course may help build a foundation for Research Scientists by teaching them the foundations of PyTorch, a powerful tool for building deep learning models. Beyond that, learning the concepts taught in this course can help a Research Scientist explore complex deep learning models for any purpose.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers work on all aspects of artificial intelligence, from designing and building to testing and deploying. This course may help build a foundation for Artificial Intelligence Engineers by teaching them the foundations of PyTorch, a powerful tool for building deep learning models. Beyond that, learning the concepts taught in this course can help an Artificial Intelligence Engineer build and deploy applications that use these models.
Data Scientist
Data Scientist
Data Scientists use their knowledge of math, statistics, programming, and artificial intelligence to make sense of the vast amounts of data that businesses collect. This course may help build a foundation for Data Scientists by helping them build skills in tensor calculus and computational graph building. Furthermore, learning the techniques taught in this course can help a Data Scientist create and iterate on machine learning models for any purpose.
Data Analyst
Data Analyst
Data Analysts use their knowledge of math, statistics, and programming to make sense of the vast amounts of data that businesses collect. This course may help build a foundation for Data Analysts by helping them build skills in tensor calculus and computational graph building. Furthermore, learning the techniques taught in this course can help a Data Analyst create and iterate on machine learning models for any purpose.
Data Engineer
Data Engineer
Data Engineers work on all aspects of data engineering, from designing and building to testing and deploying data pipelines. This course may help build a foundation for Data Engineers by teaching them the fundamentals of PyTorch. Learning the concepts taught in this course can help a Data Engineer build and deploy data pipelines that use PyTorch models.
Consultant
Consultant
Consultants work on all aspects of consulting, from providing advice to developing and implementing solutions. This course may help build a foundation for Consultants by teaching them the fundamentals of PyTorch. Learning the concepts taught in this course can help a Consultant understand the technical implications of using PyTorch models in their solutions.
Business Analyst
Business Analyst
Business Analysts work on all aspects of business analysis, from gathering and analyzing requirements to developing and implementing solutions. This course may help build a foundation for Business Analysts by teaching them the fundamentals of PyTorch. Learning the concepts taught in this course can help a Business Analyst understand the technical implications of using PyTorch models in their solutions.
Product Manager
Product Manager
Product Managers work on all aspects of product management, from defining the product vision to launching and iterating on the product. This course may help build a foundation for Product Managers by teaching them the fundamentals of PyTorch. Learning the concepts taught in this course can help a Product Manager understand the technical implications of using PyTorch models in their products.
Project Manager
Project Manager
Project Managers work on all aspects of project management, from planning and executing to monitoring and closing projects. This course may help build a foundation for Project Managers by teaching them the fundamentals of PyTorch. Learning the concepts taught in this course can help a Project Manager understand the technical implications of using PyTorch models in their projects.
Technical Writer
Technical Writer
Technical Writers work on all aspects of technical writing, from writing user manuals to developing training materials. This course may help build a foundation for Technical Writers by teaching them the fundamentals of PyTorch. Learning the concepts taught in this course can help a Technical Writer understand the technical implications of using PyTorch models in their documentation.
For more career information including salaries, visit:
OpenCourser.com/course/y48jhq/foundations
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Foundations of PyTorch.
Provides a comprehensive overview of deep learning with PyTorch, covering the fundamentals of deep learning, the PyTorch library, and advanced techniques such as transfer learning and natural language processing.
Provides a quick and easy introduction to deep learning with PyTorch, covering the basics of deep learning and the PyTorch library.
Provides a comprehensive overview of deep learning with Python, covering the fundamentals of deep learning, the TensorFlow library, and advanced techniques such as transfer learning and natural language processing.
Provides a comprehensive overview of neural networks and deep learning, covering the fundamentals of neural networks, the history of deep learning, and advanced techniques such as convolutional neural networks and recurrent neural networks.
Provides a visual and intuitive introduction to deep learning, covering the basics of deep learning and the TensorFlow library.
Provides a comprehensive overview of statistical learning, covering the fundamentals of statistical learning, the history of statistical learning, and advanced techniques such as deep learning and reinforcement learning.
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Provides a comprehensive overview of pattern recognition and machine learning, covering the fundamentals of pattern recognition and machine learning, the history of pattern recognition and machine learning, and advanced techniques such as deep learning and reinforcement learning.
Provides a practical guide to deep learning with Fastai and PyTorch, covering topics such as data preparation, model building, training, and evaluation.
Provides a comprehensive overview of deep learning with TensorFlow, covering the fundamentals of deep learning, the TensorFlow library, and advanced techniques such as transfer learning and natural language processing.
Provides a comprehensive overview of machine learning with Scikit-Learn, Keras, and TensorFlow, covering the fundamentals of machine learning, the history of machine learning, and advanced techniques such as deep learning and reinforcement learning.
Provides a comprehensive overview of machine learning with R, covering the fundamentals of machine learning, the history of machine learning, and advanced techniques such as deep learning and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y48jhq/foundations
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Time
2 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a
collection
called
Building Deep Learning Solutions with PyTorch. It's comprised of eight courses:
- Foundations of PyTorch (viewing)
- Building Your First PyTorch Solution
- Using PyTorch in the Cloud: PyTorch Playbook
- Image Classification with PyTorch
- Style Transfer with PyTorch
- Natural Language Processing with PyTorch
- Expediting Deep Learning with Transfer Learning: PyTorch Playbook
- Deploying PyTorch Models in Production: PyTorch Playbook
Good to know
Designed for aspiring and practicing deep learning engineers
Weakens the foundation of a static graph, which is industry standard
Instructors have proven track record in building deep learning models
Builds strong skills in constructing dynamic computation graphs, which are crucial for deep learning models
Emphasizes Python-native support for debugging and visualization, which is preferred in the industry
Uses Tensors, Variables, and Gradients, which are essential concepts for deep learning in PyTorch
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