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Luis Serrano,
Alexis Cook,
Soumith Chintala,
Cezanne Camacho,
and
Mat Leonard
Take Udacity's free Introduction to PyTorch course and learn the basics of deep learning. Implement your own deep neural networks with PyTorch. Learn online with Udacity.
Register for this course and see more details by visiting:
OpenCourser.com/course/gmekn3/intro
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What's inside
Syllabus
Welcome to this course on deep learning with PyTorch!
Learn the concepts behind how neural networks operate and how we train them using data.
Hear from Soumith Chintala, the creator of PyTorch, about the past, present, and future of the PyTorch framework.
Read more
Syllabus
Welcome to this course on deep learning with PyTorch!
Learn the concepts behind how neural networks operate and how we train them using data.
Hear from Soumith Chintala, the creator of PyTorch, about the past, present, and future of the PyTorch framework.
Read more
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.
Learn how to use convolutional neural networks to build state-of-the-art computer vision models.
Use a deep neural network to transfer the artistic style of one image onto another image.
Learn how to use recurrent neural networks to learn from sequential data such as text. Build a network that can generate realistic text one letter at a time.
Here you'll build a recurrent neural network that can accurately predict the sentiment of movie reviews.
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
Explores deep learning principles and their applications in computer vision and natural language processing
Taught by renowned instructors from industry and academia, including Soumith Chintala, the creator of PyTorch
Develops practical skills in building and training deep neural networks using PyTorch
Covers advanced topics such as convolutional neural networks, recurrent neural networks, and language models
Prepares learners for careers in deep learning and artificial intelligence
Requires basic programming knowledge and familiarity with Python
Save this course
Save Intro to Deep Learning with PyTorch to your list so you can find it easily later:
Save
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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 Intro to Deep Learning with PyTorch with these
activities:
Follow a Tutorial on PyTorch Image Classification
Show steps
Gain hands-on experience and reinforce the foundational concepts of PyTorch by implementing an image classification project.
Browse courses on
Computer Vision
Show steps
-
Follow the tutorial step-by-step to build a neural network for image classification.
-
Tweak the model's parameters and architecture to enhance its accuracy.
Practice Creating Neural Networks
Show steps
Implement the theoretical concepts of neural network construction and training you learn in the course into working PyTorch code.
Browse courses on
PyTorch
Show steps
-
Implement the concepts of backpropagation and gradient descent to train a neural network.
-
Experiment with different neural network architectures and activation functions to improve performance.
Build a Dataset of Your Own Deep Learning Projects
Show steps
Strengthen your understanding of data requirements and the diversity of deep learning applications by creating a personal dataset.
Browse courses on
Deep Learning Applications
Show steps
-
Choose a specific area of interest for your dataset, such as image recognition, natural language processing, or time series analysis.
-
Collect and organize data from various sources, ensuring diversity and representativeness.
-
Label and annotate the data appropriately based on the task you aim to address.
-
Share your dataset with the community for potential collaborations and further research.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve PyTorch Coding Challenges
Show steps
Test your understanding of PyTorch's syntax and apply it to practical problem-solving.
Browse courses on
Deep Learning
Show steps
-
Attempt challenges that require you to manipulate tensors and implement PyTorch operations.
-
Analyze and debug code to identify and fix errors.
-
Compare your solutions with others to learn from alternative approaches.
Create a Visual Explanation of a Deep Learning Concept
Show steps
Deepen your understanding and improve your communication skills by explaining a deep learning concept through a visual metaphor or analogy.
Show steps
-
Choose a deep learning concept you want to explain, such as backpropagation or convolutional neural networks.
-
Brainstorm and sketch visual representations, such as diagrams, animations, or infographics.
-
Create a clear and engaging visual explanation that simplifies the concept.
Attend a PyTorch Workshop or Hackathon
Show steps
Immerse yourself in a collaborative and hands-on learning environment where you can connect with experts and work on real-world deep learning projects.
Browse courses on
Hands-On Learning
Show steps
-
Identify and register for a PyTorch workshop or hackathon in your area.
-
Prepare for the event by reviewing relevant concepts and completing any prerequisites.
-
Actively participate in the event, ask questions, and collaborate with other attendees.
-
Follow up on the connections and insights gained from the event.
Follow a Tutorial on PyTorch Image Classification
Show steps
Gain hands-on experience and reinforce the foundational concepts of PyTorch by implementing an image classification project.
Browse courses on
Computer Vision
Show steps
Show steps
Show steps
- Follow the tutorial step-by-step to build a neural network for image classification.
- Tweak the model's parameters and architecture to enhance its accuracy.
Practice Creating Neural Networks
Show steps
Implement the theoretical concepts of neural network construction and training you learn in the course into working PyTorch code.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Implement the concepts of backpropagation and gradient descent to train a neural network.
- Experiment with different neural network architectures and activation functions to improve performance.
Build a Dataset of Your Own Deep Learning Projects
Show steps
Strengthen your understanding of data requirements and the diversity of deep learning applications by creating a personal dataset.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Choose a specific area of interest for your dataset, such as image recognition, natural language processing, or time series analysis.
- Collect and organize data from various sources, ensuring diversity and representativeness.
- Label and annotate the data appropriately based on the task you aim to address.
- Share your dataset with the community for potential collaborations and further research.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve PyTorch Coding Challenges
Show steps
Test your understanding of PyTorch's syntax and apply it to practical problem-solving.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Attempt challenges that require you to manipulate tensors and implement PyTorch operations.
- Analyze and debug code to identify and fix errors.
- Compare your solutions with others to learn from alternative approaches.
Create a Visual Explanation of a Deep Learning Concept
Show steps
Deepen your understanding and improve your communication skills by explaining a deep learning concept through a visual metaphor or analogy.
Show steps
Show steps
Show steps
- Choose a deep learning concept you want to explain, such as backpropagation or convolutional neural networks.
- Brainstorm and sketch visual representations, such as diagrams, animations, or infographics.
- Create a clear and engaging visual explanation that simplifies the concept.
Attend a PyTorch Workshop or Hackathon
Show steps
Immerse yourself in a collaborative and hands-on learning environment where you can connect with experts and work on real-world deep learning projects.
Browse courses on
Hands-On Learning
Show steps
Show steps
Show steps
- Identify and register for a PyTorch workshop or hackathon in your area.
- Prepare for the event by reviewing relevant concepts and completing any prerequisites.
- Actively participate in the event, ask questions, and collaborate with other attendees.
- Follow up on the connections and insights gained from the event.
Career center
Learners who complete Intro to Deep Learning with PyTorch will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/gmekn3/intro
Reading list
We've selected three 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
Intro to Deep Learning with PyTorch.
Provides a comprehensive overview of deep learning, covering the basics of neural networks, convolutional neural networks, recurrent neural networks, and more. It valuable resource for anyone looking to learn more about deep learning.
Provides a practical introduction to machine learning, covering a wide range of topics including data preprocessing, feature engineering, model selection, and model evaluation. It valuable resource for anyone looking to learn more about machine learning.
Provides a comprehensive overview of deep learning for natural language processing, covering a wide range of topics including text classification, machine translation, and question answering. It valuable resource for anyone looking to learn more about deep learning for NLP.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/gmekn3/intro
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Effort
1 day
Via
Udacity
Instructors
Luis Serrano
Alexis Cook
Soumith Chintala
Cezanne Camacho
Mat Leonard
Language
English
Good to know
Explores deep learning principles and their applications in computer vision and natural language processing
Taught by renowned instructors from industry and academia, including Soumith Chintala, the creator of PyTorch
Develops practical skills in building and training deep neural networks using PyTorch
Covers advanced topics such as convolutional neural networks, recurrent neural networks, and language models
Prepares learners for careers in deep learning and artificial intelligence
Requires basic programming knowledge and familiarity with Python
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