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Parth Dhameliya
In this two hour project-based course, you will get to know basic components to create convolutional neural network using pytorch through hands-on tasks. You will learn how to create, train and evaluate a convolutional neural network using pytorch. By the end...
Read more
In this two hour project-based course, you will get to know basic components to create convolutional neural network using pytorch through hands-on tasks. You will learn how to create, train and evaluate a convolutional neural network using pytorch. By the end...
Read more
In this two hour project-based course, you will get to know basic components to create convolutional neural network using pytorch through hands-on tasks. You will learn how to create, train and evaluate a convolutional neural network using pytorch. By the end of this project, you will be able to build and train a convolutional neural network on CIFAR-10 dataset.
This guided project is for learners who want to use pytorch for building deep learning models. Learners who have basic understanding of convolutional neural network and want to apply using a deep learning framework like pytorch. This project provides learners with deep 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/cr60cf/deep
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Guides learners in structuring a Convolutional Neural Network from scratch through practical tasks
Offers step-by-step guidance on building and training a Convolutional Neural Network using PyTorch
Provides hands-on experience with essential components of PyTorch for developing deep learning models
Focuses on basic understanding of Convolutional Neural Networks and their implementation with PyTorch
Assumes familiarity with Python and Neural Networks before enrolling
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Reviews summary
Beginner-friendly deep learning with pytorch
The "Deep Learning with PyTorch: Convolutional Neural Network" course is well-received by learners seeking an introduction to using PyTorch for deep learning models. With clear explanations and a practical project-based approach, this course helps learners build a solid foundation in PyTorch basics.
Explanations and pace are suitable for beginners.
"Good course for beginners"
"Easy to understand explanation with proper pace."
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 : Convolutional Neural Network with these
activities:
Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
Show steps
Solidify learning with foundational concepts in neural networks.
View
Deep Learning
on Amazon
Show steps
-
Read through Chapter 10: Convolutional Neural Networks.
-
Complete the practice problems at the end of the chapter.
-
Implement a CNN model using PyTorch.
PyTorch Tutorials for Convolutional Neural Networks
Show steps
Gain practical knowledge in applying PyTorch for building CNNs.
Browse courses on
PyTorch
Show steps
-
Follow the official PyTorch tutorials on CNNs.
-
Try out different network architectures and experiment with hyperparameters.
-
Implement data augmentation techniques to enhance model performance.
Peer Coding and Debugging Sessions
Show steps
Gain insights and support from peers while working on CNN projects.
Browse courses on
CNN
Show steps
-
Form a study group with fellow learners.
-
Set up regular sessions to work on CNN projects together.
-
Share ideas, troubleshoot problems, and learn from each other.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Kaggle Convolutional Neural Network Competition
Show steps
Sharpen skills in building and training CNN models.
Browse courses on
CNN
Show steps
-
Select a suitable Kaggle competition focused on CNN.
-
Build and train a CNN model using PyTorch.
-
Evaluate and fine-tune the model for better performance.
Build a CNN-based Image Classifier
Show steps
Apply CNN knowledge to develop a practical application.
Browse courses on
CNN
Show steps
-
Choose a dataset and prepare the data for training.
-
Design and implement a CNN model using PyTorch.
-
Train and evaluate the model on the dataset.
Explain Convolutional Neural Networks to a New Audience
Show steps
Deepen understanding of CNNs by teaching them to others.
Browse courses on
Convolutional Neural Networks
Show steps
-
Choose an accessible platform for explaining CNNs (e.g., blog, presentation).
-
Break down CNN concepts into simple terms.
-
Use visual aids and examples to illustrate how CNNs work.
Hackathon: Build a CNN-based Solution to a Real-World Problem
Show steps
Test skills and push limits by competing in a CNN-focused hackathon.
Browse courses on
CNN
Show steps
-
Identify a real-world problem that can be addressed using CNNs.
-
Form a team and develop a project plan.
-
Build and implement a CNN-based solution during the hackathon.
Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville
Show steps
Solidify learning with foundational concepts in neural networks.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read through Chapter 10: Convolutional Neural Networks.
- Complete the practice problems at the end of the chapter.
- Implement a CNN model using PyTorch.
PyTorch Tutorials for Convolutional Neural Networks
Show steps
Gain practical knowledge in applying PyTorch for building CNNs.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Follow the official PyTorch tutorials on CNNs.
- Try out different network architectures and experiment with hyperparameters.
- Implement data augmentation techniques to enhance model performance.
Peer Coding and Debugging Sessions
Show steps
Gain insights and support from peers while working on CNN projects.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Form a study group with fellow learners.
- Set up regular sessions to work on CNN projects together.
- Share ideas, troubleshoot problems, and learn from each other.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Kaggle Convolutional Neural Network Competition
Show steps
Sharpen skills in building and training CNN models.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Select a suitable Kaggle competition focused on CNN.
- Build and train a CNN model using PyTorch.
- Evaluate and fine-tune the model for better performance.
Build a CNN-based Image Classifier
Show steps
Apply CNN knowledge to develop a practical application.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Choose a dataset and prepare the data for training.
- Design and implement a CNN model using PyTorch.
- Train and evaluate the model on the dataset.
Explain Convolutional Neural Networks to a New Audience
Show steps
Deepen understanding of CNNs by teaching them to others.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Choose an accessible platform for explaining CNNs (e.g., blog, presentation).
- Break down CNN concepts into simple terms.
- Use visual aids and examples to illustrate how CNNs work.
Hackathon: Build a CNN-based Solution to a Real-World Problem
Show steps
Test skills and push limits by competing in a CNN-focused hackathon.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using CNNs.
- Form a team and develop a project plan.
- Build and implement a CNN-based solution during the hackathon.
Career center
Learners who complete Deep Learning with PyTorch : Convolutional Neural Network will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer systems that can interpret and understand images and videos. With the knowledge learned in this course, you will be able to create convolutional neural networks that can be used for a variety of computer vision tasks, such as object detection, image classification, and facial recognition. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Computer Vision Engineer.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and develop machine learning models that can learn from data and make predictions. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Machine Learning Engineer.
Deep Learning Scientist
Deep Learning Scientist
Deep Learning Scientists research and develop new deep learning algorithms and techniques. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of deep learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Deep Learning Scientist.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Data Scientist.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Software Engineer.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including computer science, engineering, and medicine. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Research Scientist.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Product Manager.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Business Analyst.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Data Analyst.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make better decisions. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Statistician.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data to help businesses make better investment decisions. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Financial Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to help businesses make better operational decisions. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as an Operations Research Analyst.
Market Research Analyst
Market Research Analyst
Market Research Analysts collect and analyze data to help businesses understand their customers and make better marketing decisions. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Market Research Analyst.
Sales Engineer
Sales Engineer
Sales Engineers help customers understand and implement complex technical solutions. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Sales Engineer.
Technical Writer
Technical Writer
Technical Writers create and maintain technical documentation, such as user manuals, white papers, and training materials. This course will teach you how to create, train, and evaluate convolutional neural networks using pytorch, which are a powerful type of machine learning model that is widely used in a variety of applications, such as image classification, object detection, and natural language processing. This course will help you build a strong foundation in the fundamentals of deep learning and pytorch, which are essential skills for a successful career as a Technical Writer.
For more career information including salaries, visit:
OpenCourser.com/course/cr60cf/deep
Reading list
We've selected eight 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 : Convolutional Neural Network .
Save
Comprehensive text on deep learning, making it suitable for readers who want to gain a deeper understanding of the theoretical foundations of deep learning and its advanced techniques.
Provides a comprehensive overview of deep learning with PyTorch, covering topics such as neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for learners who want to gain a deeper understanding of deep learning and how to apply it using PyTorch.
Covers data science, machine learning, and deep learning in Python. It provides a solid introduction to the foundations of machine learning and deep learning.
Provides a comprehensive overview of deep learning for natural language processing. It covers topics such as natural language understanding, natural language generation, and machine translation. It good choice for learners who want to gain a deeper understanding of deep learning and how to apply it to natural language processing.
Save
Provides a comprehensive overview of deep learning with R. It covers topics such as neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. It good choice for learners who want to gain a deeper understanding of deep learning and how to apply it using R.
Provides a comprehensive overview of machine learning with PyTorch and Scikit-Learn. It covers topics such as supervised learning, unsupervised learning, and deep learning. It good choice for learners who want to gain a deeper understanding of machine learning and how to apply it using PyTorch and Scikit-Learn.
Includes recipes that cover a wide range of topics, including data preprocessing, model selection, and evaluation. It uses Python and scikit-learn.
Provides a comprehensive overview of deep learning with Python. It covers topics such as neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. It good choice for learners who want to gain a deeper understanding of deep learning and how to apply it using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cr60cf/deep
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Good to know
Guides learners in structuring a Convolutional Neural Network from scratch through practical tasks
Offers step-by-step guidance on building and training a Convolutional Neural Network using PyTorch
Provides hands-on experience with essential components of PyTorch for developing deep learning models
Focuses on basic understanding of Convolutional Neural Networks and their implementation with PyTorch
Assumes familiarity with Python and Neural Networks before enrolling
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