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Parth Dhameliya
In this one-hour project-based course, you will get to know the basic components of pytorch through hands-on tasks. You will learn how to define, train, and evaluate a neural network with pytorch. By the end of this project, you will build a neural network which can classify handwritten digits. You will be able to create a neural network using pytorch and complete classification tasks in deep learning with pytorch.
This guided project is for learners who want to use pytorch for building deep learning models. Learners who have a basic understanding of deep neural networks and want to apply neural network using deep learning...
Read more
In this one-hour project-based course, you will get to know the basic components of pytorch through hands-on tasks. You will learn how to define, train, and evaluate a neural network with pytorch. By the end of this project, you will build a neural network which can classify handwritten digits. You will be able to create a neural network using pytorch and complete classification tasks in deep learning with pytorch.
This guided project is for learners who want to use pytorch for building deep learning models. Learners who have a basic understanding of deep neural networks and want to apply neural network using deep learning...
Read more
In this one-hour project-based course, you will get to know the basic components of pytorch through hands-on tasks. You will learn how to define, train, and evaluate a neural network with pytorch. By the end of this project, you will build a neural network which can classify handwritten digits. You will be able to create a neural network using pytorch and complete classification tasks in deep learning with pytorch.
This guided project is for learners who want to use pytorch for building deep learning models. Learners who have a basic understanding of deep neural networks and want to apply neural network using deep learning framework like pytorch. This project provides learners with deeper knowledge about the basics of pytorch and its main components.
In order to be successful in this project, you should be familiar with python and neural networks.
Register for this course and see more details by visiting:
OpenCourser.com/course/cj0beq/deep
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for those who already have a basic knowledge of deep neural networks and want to apply a neural network using deep learning framework like pytorch in a practical project
Appropriate for learners seeking to develop a deeper understanding of PyTorch and its foundational elements
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Reviews summary
Pytorch neural network basics
Deep Learning with PyTorch: Build a Neural Network is a one-hour, project-based course on the basics of PyTorch for deep learning. Most learners who left reviews reported that the course helped deepen their understanding of the basics of PyTorch. However, a few learners reported that the course was not helpful because the instructor simply read tutorial code out loud.
Well-explained parameters and functions.
"Each parameter and function are explained very well."
Project-based, hands-on
"In this one-hour project-based course, you will get to know the basic components of pytorch through hands-on tasks."
Read tutorial code out loud
"Basically just some tutorial code read out loud without any background information on classes/objects."
Lacked background info
"Not enough background explanation on the theory, function usage and result analysis."
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 Deep Learning with PyTorch: Build a Neural Network with these
activities:
Organize course materials and resources
Show steps
Promotes organization and aids in knowledge retention
Show steps
-
Download and organize lecture slides, notes, and assignments
-
Create a study guide or summary of key concepts
-
Set up a system for reviewing and revisiting materials
Find experienced PyTorch practitioners
Show steps
Provides access to guidance and expertise from industry professionals
Browse courses on
PyTorch
Show steps
-
Attend industry events and conferences
-
Reach out to professionals on LinkedIn
-
Join online communities and forums
Work through the PyTorch tutorials
Show steps
Develops practical skills in using PyTorch for deep learning
Browse courses on
PyTorch
Show steps
-
Follow the official PyTorch tutorials
-
Build a simple neural network using PyTorch
-
Train and evaluate the neural network on a dataset
Six other activities
Expand to see all activities and additional details
Show all nine activities
Help other students with PyTorch
Show steps
Reinforces learning through teaching and collaboration
Browse courses on
PyTorch
Show steps
-
Join online forums or study groups
-
Answer questions and provide guidance to other learners
-
Organize study sessions or workshops
Review 'Deep Learning with PyTorch' by Eli Stevens
Show steps
Provides a comprehensive theoretical and practical introduction to PyTorch
View
Deep Learning with PyTorch: Build, train, and...
on Amazon
Show steps
-
Read chapters 1-3 to grasp the fundamentals of PyTorch
-
Complete the hands-on exercises in chapters 4-6 to apply your knowledge
-
Summarize the key concepts and techniques covered in the book
Solve PyTorch coding challenges
Show steps
Strengthens problem-solving abilities and deepens understanding of PyTorch
Browse courses on
PyTorch
Show steps
-
Find PyTorch coding challenges online
-
Attempt to solve the challenges independently
-
Review solutions and identify areas for improvement
Develop a deep learning model using PyTorch
Show steps
Applies knowledge to create a practical implementation of deep learning
Browse courses on
PyTorch
Show steps
-
Define the problem and gather data
-
Design and implement a deep learning model using PyTorch
-
Train and evaluate the model
-
Write a report documenting the project
Create a resource hub for PyTorch
Show steps
Encourages organization, research, and knowledge sharing
Browse courses on
PyTorch
Show steps
-
Gather resources such as tutorials, articles, and code snippets
-
Organize and categorize the resources
-
Share the resource hub with the community
Participate in a PyTorch hackathon
Show steps
Challenges students to apply their skills in a competitive environment
Browse courses on
PyTorch
Show steps
-
Find a hackathon that aligns with your interests
-
Form or join a team
-
Develop and submit a project using PyTorch
Organize course materials and resources
Show steps
Promotes organization and aids in knowledge retention
Show steps
Show steps
Show steps
- Download and organize lecture slides, notes, and assignments
- Create a study guide or summary of key concepts
- Set up a system for reviewing and revisiting materials
Find experienced PyTorch practitioners
Show steps
Provides access to guidance and expertise from industry professionals
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Attend industry events and conferences
- Reach out to professionals on LinkedIn
- Join online communities and forums
Work through the PyTorch tutorials
Show steps
Develops practical skills in using PyTorch for deep learning
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Follow the official PyTorch tutorials
- Build a simple neural network using PyTorch
- Train and evaluate the neural network on a dataset
Six other activities
Expand to see all activities and additional details
Show all nine activities
Help other students with PyTorch
Show steps
Reinforces learning through teaching and collaboration
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Join online forums or study groups
- Answer questions and provide guidance to other learners
- Organize study sessions or workshops
Review 'Deep Learning with PyTorch' by Eli Stevens
Show steps
Provides a comprehensive theoretical and practical introduction to PyTorch
View
Deep Learning with PyTorch: Build, train, and...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 to grasp the fundamentals of PyTorch
- Complete the hands-on exercises in chapters 4-6 to apply your knowledge
- Summarize the key concepts and techniques covered in the book
Solve PyTorch coding challenges
Show steps
Strengthens problem-solving abilities and deepens understanding of PyTorch
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Find PyTorch coding challenges online
- Attempt to solve the challenges independently
- Review solutions and identify areas for improvement
Develop a deep learning model using PyTorch
Show steps
Applies knowledge to create a practical implementation of deep learning
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Define the problem and gather data
- Design and implement a deep learning model using PyTorch
- Train and evaluate the model
- Write a report documenting the project
Create a resource hub for PyTorch
Show steps
Encourages organization, research, and knowledge sharing
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Gather resources such as tutorials, articles, and code snippets
- Organize and categorize the resources
- Share the resource hub with the community
Participate in a PyTorch hackathon
Show steps
Challenges students to apply their skills in a competitive environment
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Find a hackathon that aligns with your interests
- Form or join a team
- Develop and submit a project using PyTorch
Career center
Learners who complete Deep Learning with PyTorch: Build a Neural Network will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers implement and maintain machine learning models to solve real-world problems. They work on the entire machine learning lifecycle, from data collection and preparation to model development and deployment. This course can help Machine Learning Engineers build a strong foundation in PyTorch, which is one of the most popular deep learning frameworks. By learning how to use PyTorch to define, train, and evaluate neural networks, Machine Learning Engineers can enhance their skills and become more effective in developing and deploying machine learning solutions.
Data Scientist
Data Scientist
Data Scientists apply their knowledge of machine learning, statistical modeling, and data analysis to solve business problems. They develop predictive models and build data-driven solutions that help companies make informed decisions. This course can help Data Scientists build a foundation in PyTorch, a popular deep learning framework, which is essential for building and deploying deep learning models. By learning how to define, train, and evaluate neural networks with PyTorch, Data Scientists can enhance their skills and contribute more effectively to data-driven decision-making.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on a wide range of projects, from mobile apps to enterprise software. This course can help Software Engineers build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Software Engineers can expand their skillset and become more competitive in the job market.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. They use a variety of tools and techniques, including machine learning and statistical modeling. This course can help Data Analysts build a foundation in PyTorch, which is a powerful deep learning framework. By learning how to use PyTorch to define, train, and evaluate neural networks, Data Analysts can enhance their skills and become more effective in extracting insights from data.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including machine learning, artificial intelligence, and computer vision. They develop new algorithms and techniques to solve complex problems. This course can help Research Scientists build a foundation in PyTorch, which is a powerful deep learning framework. By learning how to use PyTorch to define, train, and evaluate neural networks, Research Scientists can enhance their skills and become more effective in conducting research.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They work in a variety of settings, including hedge funds, investment banks, and asset management firms. This course can help Quantitative Analysts build a foundation in PyTorch, which is becoming increasingly popular for building financial models. By learning how to use PyTorch to define, train, and evaluate neural networks, Quantitative Analysts can enhance their skills and become more effective in making investment decisions.
Product Manager
Product Manager
Product Managers are responsible for developing and launching new products. They work with a variety of stakeholders, including engineers, designers, and marketers. This course can help Product Managers build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Product Managers can enhance their skills and become more effective in developing data-driven products.
Business Analyst
Business Analyst
Business Analysts help businesses understand their needs and develop solutions to meet those needs. They work on a wide range of projects, from process improvement to software development. This course can help Business Analysts build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Business Analysts can enhance their skills and become more effective in developing data-driven solutions.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. They work with engineers and other subject matter experts to translate complex technical information into clear and concise language. This course can help Technical Writers build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Technical Writers can enhance their skills and become more effective in creating documentation for deep learning models.
Consultant
Consultant
Consultants help businesses solve problems and improve their operations. They work on a wide range of projects, from strategic planning to IT implementation. This course can help Consultants build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Consultants can enhance their skills and become more effective in helping businesses solve problems.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing data to help businesses understand their customers and develop effective marketing campaigns. They work in a variety of settings, including marketing agencies, advertising firms, and consumer goods companies. This course can help Marketing Analysts build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Marketing Analysts can enhance their skills and become more effective in developing data-driven marketing campaigns.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations to investors. They work in a variety of settings, including investment banks, asset management firms, and hedge funds. This course can help Financial Analysts build a foundation in PyTorch, which is becoming increasingly popular for building financial models. By learning how to use PyTorch to define, train, and evaluate neural networks, Financial Analysts can enhance their skills and become more effective in making investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. They work on a wide range of projects, from supply chain management to healthcare delivery. This course can help Operations Research Analysts build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Operations Research Analysts can enhance their skills and become more effective in solving business problems.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. They work in a variety of settings, from small startups to large corporations. This course can help Software Developers build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Software Developers can enhance their skills and become more effective in developing data-driven applications.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines that collect, store, and process data. They work in a variety of settings, from small startups to large corporations. This course can help Data Engineers build a foundation in PyTorch, which is becoming increasingly popular for building deep learning models. By learning how to use PyTorch to define, train, and evaluate neural networks, Data Engineers can enhance their skills and become more effective in building and maintaining data pipelines.
For more career information including salaries, visit:
OpenCourser.com/course/cj0beq/deep
Reading list
We've selected seven 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
Deep Learning with PyTorch: Build a Neural Network.
Save
Provides a comprehensive overview of deep learning, covering the fundamentals of neural networks, training techniques, and applications. It valuable resource for beginners and experienced practitioners alike.
Provides a comprehensive overview of deep learning for natural language processing, covering a wide range of topics from text classification to machine translation. It valuable resource for beginners and experienced practitioners alike.
Provides a comprehensive overview of deep learning, covering the fundamentals of neural networks, training techniques, and applications. It valuable resource for beginners and experienced practitioners alike.
Save
Provides a comprehensive overview of TensorFlow for deep learning, covering the fundamentals of neural networks, training techniques, and applications. It valuable resource for beginners and experienced practitioners alike.
Provides a comprehensive overview of deep learning with TensorFlow, covering the fundamentals of neural networks, training techniques, and applications. It valuable resource for beginners and experienced practitioners alike.
Provides a practical introduction to machine learning, covering a wide range of topics from data preprocessing to model evaluation. It valuable resource for beginners and experienced practitioners alike.
Provides a comprehensive overview of deep learning with Keras, covering the fundamentals of neural networks, training techniques, and applications. It valuable resource for beginners and experienced practitioners alike.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cj0beq/deep
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Good to know
Suitable for those who already have a basic knowledge of deep neural networks and want to apply a neural network using deep learning framework like pytorch in a practical project
Appropriate for learners seeking to develop a deeper understanding of PyTorch and its foundational elements
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