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Luis Serrano,
Mat Leonard,
and
Erick Galinkin
Become a neural network expert with our comprehensive PyTorch online course. Dive into Neural Network Fundamentals and gain in-depth training. Sign Up Today!
Prerequisite details
Read more
Become a neural network expert with our comprehensive PyTorch online course. Dive into Neural Network Fundamentals and gain in-depth training. Sign Up Today!
Prerequisite details
Read more
Become a neural network expert with our comprehensive PyTorch online course. Dive into Neural Network Fundamentals and gain in-depth training. Sign Up Today!
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Basic descriptive statistics
- Python for data science
- Basic probability
- Linear algebra
- Multivariable calculus
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/6tsot1/introduction
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What's inside
Syllabus
Meet your instructors, get a short overview of what you'll be learning, check your prerequisites, and learn how to use the workspaces and notebooks found throughout the lessons.
Read more
Syllabus
Meet your instructors, get a short overview of what you'll be learning, check your prerequisites, and learn how to use the workspaces and notebooks found throughout the lessons.
Read more
In this lesson, Luis will give you solid foundations on deep learning and neural networks. You'll also implement gradient descent and backpropagation in Python right here in the classroom.
Mat will introduce you to a different error function and guide you through implementing gradient descent using numpy matrix multiplication.
Now that you know what neural networks are, in this lesson you will learn several techniques to improve their training.
Learn how to use PyTorch for building deep learning models.
In this project, you'll create your own image classifier and then train—and evaluate its performance—using one of the most classic and well-studied computer vision data sets, CIFAR-10.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches key concepts of neural networks and deep learning, such as gradient descent and backpropagation
Taught by experienced instructors Luis Serrano, Mat Leonard, and Erick Galinkin, who are recognized for their work in neural networks
Involves hands-on labs and interactive materials, providing practical experience in building deep learning models with PyTorch
May require additional software or tools that are not readily available in a typical household or library
Save this course
Save Introduction to Neural Networks with PyTorch to your list so you can find it easily later:
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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 Introduction to Neural Networks with PyTorch with these
activities:
Read: Deep Learning with PyTorch
Show steps
Deepen your understanding of PyTorch by reading this book.
View
Deep Learning with PyTorch: Build, train, and...
on Amazon
Show steps
-
Obtain a copy of the book.
-
Set aside time each week to read the book.
-
Take notes as you read.
-
Complete the exercises at the end of each chapter.
Show all one activities
Read: Deep Learning with PyTorch
Show steps
Deepen your understanding of PyTorch by reading this book.
View
Deep Learning with PyTorch: Build, train, and...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Set aside time each week to read the book.
- Take notes as you read.
- Complete the exercises at the end of each chapter.
Show all one activities
Career center
Learners who complete Introduction to Neural Networks with PyTorch will develop knowledge and skills
that may be useful to these careers:
Neural Network Architect
Neural Network Architect
Neural Network Architects design and develop the neural networks that power many of today's most cutting-edge technologies. This course can help you build a solid foundation in the fundamentals of neural networks and gain hands-on experience implementing them in PyTorch, one of the leading frameworks for deep learning. With this knowledge, you'll be well-equipped to design and build your own neural networks for a variety of applications, including image recognition, natural language processing, and speech recognition.
Data Scientist
Data Scientist
Data Scientists use their expertise in statistics, machine learning, and data analysis to extract insights from large datasets. This course can help you develop the skills you need to become a successful Data Scientist, including a deep understanding of neural networks and how to use them to solve real-world problems. You'll also gain experience with PyTorch, a powerful tool for building and training neural networks.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning systems. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to develop and deploy machine learning solutions for a variety of applications.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to develop software applications that leverage the power of neural networks.
Researcher
Researcher
Researchers conduct scientific research to advance our understanding of the world. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to conduct research in a variety of fields, including computer science, artificial intelligence, and neuroscience.
Quant Analyst
Quant Analyst
Quant Analysts use mathematical and statistical models to analyze financial data. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to develop and deploy quantitative trading strategies.
Business Analyst
Business Analyst
Business Analysts use data analysis to help businesses make better decisions. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to use neural networks to solve business problems and improve decision-making.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to develop and launch products that leverage the power of neural networks.
Consultant
Consultant
Consultants provide advice and guidance to businesses and organizations. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to advise businesses on how to use neural networks to solve their business problems.
Teacher
Teacher
Teachers educate students in a variety of subjects. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to teach students about neural networks and how to use them to solve real-world problems.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other products. This course can help you build a strong foundation in the fundamentals of neural networks and gain experience implementing them in PyTorch. With this knowledge, you'll be well-equipped to write documentation for neural networks and other machine learning technologies.
Salesperson
Salesperson
Salespeople sell products and services to customers. This course may be helpful if you're interested in selling products or services that leverage neural networks. You'll gain a basic understanding of neural networks and how they can be used to solve real-world problems.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns. This course may be helpful if you're interested in marketing products or services that leverage neural networks. You'll gain a basic understanding of neural networks and how they can be used to solve real-world problems.
Customer Service Representative
Customer Service Representative
Customer Service Representatives provide support to customers. This course may be helpful if you're interested in providing support for products or services that leverage neural networks. You'll gain a basic understanding of neural networks and how they can be used to solve real-world problems.
Account Manager
Account Manager
Account Managers manage customer accounts. This course may be helpful if you're interested in managing accounts for customers who use products or services that leverage neural networks. You'll gain a basic understanding of neural networks and how they can be used to solve real-world problems.
For more career information including salaries, visit:
OpenCourser.com/course/6tsot1/introduction
Reading list
We've selected ten 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
Introduction to Neural Networks with PyTorch.
Save
Provides a comprehensive and up-to-date overview of deep learning, covering the foundations, algorithms, and applications of this field. It valuable reference for both beginners and experienced practitioners.
Provides a practical introduction to PyTorch, a popular deep learning framework. It great resource for those who want to learn how to use PyTorch to build and train deep learning models.
Provides a comprehensive overview of deep learning with Python, covering the foundations, algorithms, and applications of this field. It great resource for both beginners and experienced practitioners.
Provides a comprehensive overview of the mathematics behind machine learning, covering the foundations, algorithms, and applications of this field. It great resource for those who want to learn more about the mathematical foundations of machine learning.
Provides a comprehensive overview of convex optimization, covering the foundations, algorithms, and applications of this field. It great resource for those who want to learn more about the mathematical foundations of machine learning.
Provides a comprehensive overview of deep learning for natural language processing, covering the foundations, algorithms, and applications of this field. It great resource for both beginners and experienced practitioners.
Provides a comprehensive overview of statistical learning, covering the foundations, algorithms, and applications of this field. It great resource for both beginners and experienced practitioners.
Provides a comprehensive overview of natural language processing with Python, covering the foundations, algorithms, and applications of this field. It great resource for both beginners and experienced practitioners.
Provides a comprehensive overview of speech and language processing, covering the foundations, algorithms, and applications of this field. It great resource for both beginners and experienced practitioners.
Provides a practical introduction to machine learning for hackers, covering the foundations, algorithms, and applications of this field. It great resource for those who want to learn how to use machine learning to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6tsot1/introduction
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Effort
4 weeks
Via
Udacity
Instructors
Luis Serrano
Mat Leonard
Erick Galinkin
Language
English
Good to know
Teaches key concepts of neural networks and deep learning, such as gradient descent and backpropagation
Taught by experienced instructors Luis Serrano, Mat Leonard, and Erick Galinkin, who are recognized for their work in neural networks
Involves hands-on labs and interactive materials, providing practical experience in building deep learning models with PyTorch
May require additional software or tools that are not readily available in a typical household or library
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