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Jay Alammar,
Arpan Chakraborty,
Luis Serrano,
and
Dana Sheahen
Register for this course and see more details by visiting:
OpenCourser.com/course/73j8mo/pytorc
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What's inside
Syllabus
Learn how to use PyTorch to build and train deep neural networks. By the end of this lesson, you will build a network that can classify images of dogs and cats with state-of-the-art performance.
Read more
Syllabus
Learn how to use PyTorch to build and train deep neural networks. By the end of this lesson, you will build a network that can classify images of dogs and cats with state-of-the-art performance.
Read more
In this lesson, you'll learn about embeddings in neural networks by implementing the Word2Vec model.
Learn how to represent memory in code. Then define and train RNNs in PyTorch and apply them to tasks that involve sequential data.
In this lesson, we'll walk through a tutorial showing how to deploy PyTorch models with Torch Script.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches skills, knowledge, and tools that are highly relevant in an academic setting
Taught by Jay Alammar, a recognized expert in deep learning
Taught by Arpan Chakraborty, a recognized expert in deep learning
Taught by Luis Serrano, a recognized expert in deep learning
Taught by Dana Sheahen, a recognized expert in deep learning
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in PyTorch with these
activities:
Compile Course Resources
Show steps
Compile and review course assignments, notes, quizzes, and past exams to get a better understanding of the course content.
Show steps
-
Gather course materials
-
Organize materials by topic
-
Review materials to identify strengths and weaknesses
Review PyTorch Fundamentals
Show steps
Review the basics of PyTorch, including its syntax, data structures, and common operations, to strengthen your understanding of the framework.
Browse courses on
PyTorch
Show steps
-
Revisit PyTorch documentation
-
Complete online tutorials
-
Practice writing simple PyTorch programs
Explore Advanced Neural Network Architectures
Show steps
Explore advanced neural network architectures, such as LSTMs, GRUs, and Transformers, through guided tutorials to expand your knowledge beyond the course content.
Browse courses on
Neural Networks
Show steps
-
Identify different neural network architectures
-
Follow tutorials on implementing these architectures in PyTorch
-
Experiment with different hyperparameters
Show all three activities
Compile Course Resources
Show steps
Compile and review course assignments, notes, quizzes, and past exams to get a better understanding of the course content.
Show steps
Show steps
Show steps
- Gather course materials
- Organize materials by topic
- Review materials to identify strengths and weaknesses
Review PyTorch Fundamentals
Show steps
Review the basics of PyTorch, including its syntax, data structures, and common operations, to strengthen your understanding of the framework.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Revisit PyTorch documentation
- Complete online tutorials
- Practice writing simple PyTorch programs
Explore Advanced Neural Network Architectures
Show steps
Explore advanced neural network architectures, such as LSTMs, GRUs, and Transformers, through guided tutorials to expand your knowledge beyond the course content.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Identify different neural network architectures
- Follow tutorials on implementing these architectures in PyTorch
- Experiment with different hyperparameters
Show all three activities
Career center
Learners who complete PyTorch will develop knowledge and skills
that may be useful to these careers:
Machine Learning Researcher
Machine Learning Researcher
A Machine Learning Researcher designs and builds machine learning algorithms and models. They also develop new techniques for training and evaluating models. This course can help build a foundation for a career as a Machine Learning Researcher by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of machine learning.
Deep Learning Engineer
Deep Learning Engineer
A Deep Learning Engineer designs, develops, and deploys deep learning models. They also work on improving the performance of existing models. This course can help build a foundation for a career as a Deep Learning Engineer by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of deep learning.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. They use a variety of techniques, including machine learning, to extract insights from data. This course can help build a foundation for a career as a Data Scientist by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of data science.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. They use a variety of programming languages and technologies to create software that meets the needs of users. This course can help build a foundation for a career as a Software Engineer by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of software engineering.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer designs, develops, and deploys computer vision systems. They use a variety of techniques, including deep learning, to create systems that can see and understand the world around them. This course can help build a foundation for a career as a Computer Vision Engineer by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of computer vision.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs, develops, and deploys natural language processing systems. They use a variety of techniques, including deep learning, to create systems that can understand and generate human language. This course can help build a foundation for a career as a Natural Language Processing Engineer by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of natural language processing.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and deploys artificial intelligence systems. They use a variety of techniques, including deep learning, to create systems that can think and learn like humans. This course can help build a foundation for a career as an Artificial Intelligence Engineer by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of artificial intelligence.
Statistician
Statistician
A Statistician uses data to solve problems in a variety of fields, including finance, healthcare, and marketing. They use a variety of techniques, including machine learning, to analyze data and draw conclusions. This course can help build a foundation for a career as a Statistician by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of statistics.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They use a variety of techniques, including deep learning, to create models that can learn from data and make predictions. This course can help build a foundation for a career as a Machine Learning Engineer by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of machine learning.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses data to make investment decisions. They use a variety of techniques, including machine learning, to analyze data and make predictions about the future. This course can help build a foundation for a career as a Quantitative Analyst by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of quantitative analysis.
Data Analyst
Data Analyst
A Data Analyst uses data to solve business problems. They use a variety of techniques, including machine learning, to extract insights from data. This course can help build a foundation for a career as a Data Analyst by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of data analysis.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. They use a variety of techniques, including machine learning, to analyze data and make predictions about the future. This course can help build a foundation for a career as a Financial Analyst by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of financial analysis.
Business Analyst
Business Analyst
A Business Analyst uses data to solve business problems. They use a variety of techniques, including machine learning, to extract insights from data. This course can help build a foundation for a career as a Business Analyst by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge is essential for anyone who wants to work in the field of business analysis.
Product Manager
Product Manager
A Product Manager plans and executes the development of products. They use a variety of techniques, including machine learning, to understand customer needs and develop products that meet those needs. This course can help build a foundation for a career as a Product Manager by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge can be helpful for developing products that involve data analysis or machine learning.
Project Manager
Project Manager
A Project Manager plans and executes projects. They use a variety of techniques, including machine learning, to manage resources and track progress. This course can help build a foundation for a career as a Project Manager by providing a comprehensive overview of the PyTorch framework. The course covers topics such as deep neural networks, embeddings, and recurrent neural networks. This knowledge can be helpful for managing projects that involve data analysis or machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/73j8mo/pytorc
Reading list
We've selected six 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
PyTorch.
Provides a comprehensive introduction to PyTorch, covering the basics of deep learning, neural networks, and PyTorch's API. It valuable resource for anyone looking to build and train deep neural networks with PyTorch.
Save
Provides a comprehensive introduction to deep learning, a branch of artificial intelligence that has revolutionized many industries. It valuable resource for anyone who wants to learn the basics of deep learning.
Provides a comprehensive introduction to natural language processing with PyTorch, covering topics such as text preprocessing, tokenization, word embeddings, and neural language models. It valuable resource for anyone looking to build NLP applications with PyTorch.
Provides a comprehensive introduction to transformers, a type of neural network that has revolutionized natural language processing. It covers the basics of transformers, as well as how to use them with PyTorch.
Provides a comprehensive introduction to reinforcement learning, a branch of artificial intelligence that allows computers to learn by trial and error. It valuable resource for anyone who wants to learn the basics of reinforcement learning.
Provides a comprehensive introduction to Bayesian data analysis, a statistical method that uses Bayes' theorem to update beliefs in the light of new evidence. It valuable resource for anyone who wants to learn how to use Bayesian data analysis to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/73j8mo/pytorc
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Time
390 hours
Level
Advanced
Via
Udacity
Instructors
Jay Alammar
Arpan Chakraborty
Luis Serrano
Dana Sheahen
Language
English
Good to know
Teaches skills, knowledge, and tools that are highly relevant in an academic setting
Taught by Jay Alammar, a recognized expert in deep learning
Taught by Arpan Chakraborty, a recognized expert in deep learning
Taught by Luis Serrano, a recognized expert in deep learning
Taught by Dana Sheahen, a recognized expert in deep learning
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