Brief Introduction
Deep learning is driving the AI revolution and PyTorch is making it easier than ever for anyone to build deep learning applications. In this course, you’ll gain practical experience building and training deep neural networks using PyTorch. You’ll be able to use these skills on your own personal projects.Course Summary
Learn how to use PyTorch to build deep learning models for computer vision and natural language processing tasks.Key Learning Points
- Understand the fundamentals of deep learning and PyTorch
- Build and train neural networks using PyTorch
- Apply deep learning to computer vision and natural language processing tasks
Job Positions & Salaries of people who have taken this course might have
- Deep Learning Engineer
- USA: $120,000
- India: â‚ą10,00,000
- Spain: €45,000
- Computer Vision Engineer
- USA: $110,000
- India: â‚ą8,00,000
- Spain: €40,000
- Natural Language Processing Engineer
- USA: $130,000
- India: â‚ą12,00,000
- Spain: €50,000
Related Topics for further study
Learning Outcomes
- Understand the fundamentals of deep learning and PyTorch
- Build and train neural networks using PyTorch
- Apply deep learning to computer vision and natural language processing tasks
Prerequisites or good to have knowledge before taking this course
- Familiarity with Python programming language
- Basic knowledge of linear algebra and calculus
Course Difficulty Level
IntermediateCourse Format
- Self-paced online course
- Video lectures
- Hands-on projects
Similar Courses
- Deep Learning
- Applied Data Science with Python
Related Education Paths
Notable People in This Field
- Andrew Ng
- Yann LeCun
Related Books
Description
Learn the basics of deep learning and implement your own deep neural networks with PyTorchRequirements
- To succeed in this course, you’ll need to be comfortable with Python and data processing libraries such as NumPy and Matplotlib. Basic knowledge of linear algebra and calculus is recommended, but isn’t required to complete the exercises. See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Introduction to Deep Learning Discover the basic concepts of deep learning such as neural networks and gradient descent Implement a neural network in NumPy and train it using gradient descent with in-class programming exercises Build a neural network to predict student admissions lesson 2 Introduction to PyTorch Hear from Soumith Chintala the creator of PyTorch how the framework came to be where it’s being used now and how it’s changing the future of deep learning lesson 3 Deep Learning with PyTorch Build your first neural network with PyTorch to classify images of clothing Work through a set of Jupyter Notebooks to learn the major components of PyTorch Load a pre-trained neural network to build a state-of-the-art image classifier lesson 4 Convolutional Neural Networks Use PyTorch to build Convolutional Neural Networks for state-of-the-art computer vision applications Train a convolutional network to classify dog breeds from images of dogs lesson 5 Style Transfer Use a pre-trained convolutional network to create new art by merging the style of one image with the content of another image Implement the paper "A Neural Algorithm of Artistic Style” by Leon A. Gatys Alexander S. Ecker and Matthias Bethge" lesson 6 Recurrent Neural Networks Build recurrent neural networks with PyTorch that can learn from sequential data such as natural language Implement a network that learns from Tolstoy’s Anna Karenina to generate new text based on the novel lesson 7 Natural Language Classification Use PyTorch to implement a recurrent neural network that can classify text Use your network to predict the sentiment of movie reviews lesson 8 Deploying with PyTorch Soumith Chintala teaches you how to deploy deep learning models with PyTorch Build a chatbot and compile the network for deployment in a production environment
Summary of User Reviews
Discover the powerful world of Deep Learning with PyTorch through this comprehensive course! Users praise the course for its engaging content and hands-on approach to learning. One key aspect that many users thought was good was the clear explanations and examples provided by the instructor.Pros from User Reviews
- Engaging and hands-on content
- Clear explanations and examples provided by the instructor
- Great introduction to PyTorch for beginners
- In-depth coverage of deep learning concepts
Cons from User Reviews
- Some users found the pace of the course to be too slow
- Lack of advanced topics for experienced users
- Limited interactivity in the course material
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