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Deep Learning with PyTorch

Generative Adversarial Network

Parth Dhameliya

In this two hour project-based course, you will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits. You will create a generator that will learn to generate images that look real and a discriminator that will learn to tell real images apart from fakes. This hands-on-project will provide you the detail information on how to implement such network and train to generate handwritten digit images.

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In this two hour project-based course, you will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits. You will create a generator that will learn to generate images that look real and a discriminator that will learn to tell real images apart from fakes. This hands-on-project will provide you the detail information on how to implement such network and train to generate handwritten digit images.

In order to be successful in this project, you will need to have a theoretical understanding on convolutional neural network and optimization algorithm like Adam or gradient descent. This project will focus more on the practical aspect of DCGAN and less on theoretical aspect.

Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

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What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Focuses on implementing Deep Convolutional Generative Adversarial Networks (DCGANs) in PyTorch to develop handwritten digits
Builds a foundation in implementing and comprehending DCGAN architectures for real-world applications
Utilizes PyTorch, a respected deep learning framework, for implementing and training DCGANs
Provides practical knowledge and hands-on experience in building DCGANs for generating realistic handwritten digits
Suitable for learners familiar with convolutional neural networks and optimization algorithms
Course content focuses primarily on the practical aspects of DCGAN implementation, with less emphasis on theoretical concepts

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Reviews summary

Practical pytorch dcgan implementation project

According to students, this course offers a highly practical and hands-on experience in implementing Deep Convolutional Generative Adversarial Networks (DCGANs) using PyTorch. Learners particularly praise the clarity of the code and instructor's explanations, which makes it easy to follow along. It is seen as concise and efficient, fitting well within its two-hour project format, making it ideal for busy professionals. However, a significant portion of reviews highlight that the course assumes prior knowledge of deep learning fundamentals like CNNs and PyTorch, making it less suitable for absolute beginners. Some recent reviews also noted that the PyTorch version used might be slightly older, though this was generally not a showstopper for completing the project.
Well-structured for quick, efficient learning within its 2-hour duration.
"The 2-hour format is perfect for busy professionals."
"It's concise and to the point, delivering exactly what's promised."
"This is a superb short course for implementing DCGAN."
Code is well-structured and explanations are easy to follow.
"The instructor's explanation was clear and the code provided was easy to follow."
"The clarity of the code and the instructor's narration made it very engaging."
"The code is well-commented, and the practical application of DCGANs is top-notch."
Delivers a strong hands-on experience in building DCGANs with PyTorch.
"This project was exactly what I needed to get hands-on with DCGANs in PyTorch."
"A very practical course. It delivered on its promise of focusing on implementation rather than theory."
"The hands-on coding aspect is very effective, perfect for adding a practical GAN project to my portfolio."
Some users noted the PyTorch version used might not be the latest.
"My only minor gripe is that the PyTorch version seemed slightly older, but it wasn't a showstopper."
"The PyTorch version used in labs could be updated to the latest, but it didn't break anything."
"I found the environment setup in Coursera's lab sometimes a bit slow."
Course intentionally focuses on practical application, not deep theoretical concepts.
"The theoretical background is really minimal. If you don't know GANs, this won't teach you the intuition behind them."
"The explanation of GAN theory was very minimal, as stated, but I wish there was a bit more."
Requires a solid understanding of Deep Learning and PyTorch basics.
"It assumes you know CNNs, which is fair."
"If you're not super comfortable with PyTorch or don't have a strong grasp of neural networks, you might struggle."
"I came into this with some basic Python but not much deep learning. Found it very difficult to follow. It assumes too much prior knowledge."

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 : Generative Adversarial Network with these activities:
Review Convolutional Neural Networks
Ensures a solid foundation in CNNs, which are essential for understanding DCGAN.
Show steps
  • Go through lecture notes or online resources to recap the concepts of CNNs.
  • Practice implementing simple CNN architectures in PyTorch or other frameworks.
  • Review the mathematical operations behind CNNs, such as convolutions, pooling, and activation functions.
Read 'Generative Adversarial Networks' by Ian Goodfellow
Provides a comprehensive overview of GANs, including DCGAN.
View Four Laws for the Artificially Intelligent on Amazon
Show steps
  • Read the introductory chapters to gain a foundational understanding of GANs.
  • Focus on the chapters dedicated to DCGAN and its applications.
  • Review the code examples and illustrations provided in the book.
Use Deep Convolutional Generative Adversarial Network to Generate Handwritten Digits
Helps lay the foundation for understanding DCGAN.
Show steps
  • Set up the environment by installing PyTorch and other required libraries.
  • Create a new project in PyTorch, and write the code for the generator and the discriminator.
  • Load the MNIST dataset.
  • Train the DCGAN model.
  • Evaluate the model's performance and generate handwritten digits.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a Hands-on DCGAN Workshop
Offers a structured and interactive learning experience.
Show steps
  • Identify and register for a reputable DCGAN workshop that aligns with your learning goals.
  • Prepare for the workshop by reviewing the provided materials and completing any prerequisites.
  • Actively participate in the workshop sessions, ask questions, and engage with the instructors.
  • Complete the hands-on exercises and projects during the workshop.
  • Follow up after the workshop by practicing the techniques learned and seeking additional resources.
Practice Implementing DCGAN in PyTorch
Provides hands-on experience with implementing DCGAN.
Show steps
  • Review the provided code and understand the implementation of the generator and discriminator networks.
  • Modify the code to experiment with different hyperparameters, such as the number of layers, the number of filters, and the learning rate.
  • Create custom data loaders to load your own dataset of images.
  • Visualize the generated images to evaluate the performance of the model.
  • Compare the performance of different DCGAN architectures and hyperparameter settings.
Build a DCGAN Model to Generate Synthetic Images
Demonstrates the application of DCGAN in generating synthetic images.
Show steps
  • Choose a specific domain or application for generating synthetic images, such as generating faces, landscapes, or medical images.
  • Collect and preprocess a dataset of real images from the chosen domain.
  • Design and implement a DCGAN model tailored to the specific domain.
  • Train the DCGAN model on the collected dataset.
  • Evaluate the performance of the model and compare it with existing methods.
Contribute to an Open-Source DCGAN Project
Provides real-world experience and exposure to best practices.
Show steps
  • Identify an open-source DCGAN project that aligns with your interests and skills.
  • Familiarize yourself with the project's codebase and documentation.
  • Propose and implement a new feature or improvement to the project.
  • Contribute your changes back to the project and engage with the community.
  • Review and provide feedback on contributions from other developers.

Career center

Learners who complete Deep Learning with PyTorch : Generative Adversarial Network will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data scientists use their understanding of neural networks and machine learning to help businesses make better decisions based on their data. This course will teach you the basics of DCGANs, which are a type of neural network that can be used to generate new data. This skill can be helpful for data scientists who want to work on projects involving image or text generation.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine learning engineers design, build, and maintain machine learning systems. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for machine learning engineers who want to work on projects involving image or text generation.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software engineers design, develop, and maintain software systems. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for software engineers who want to work on projects involving image or text generation.
See salaries and explore the career path for Software Engineer
Data Analyst
Data analysts use their understanding of data to help businesses make better decisions. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for data analysts who want to work on projects involving image or text generation.
See salaries and explore the career path for Data Analyst
Research Scientist
Research scientists conduct research in a variety of fields, including computer science, engineering, and medicine. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for research scientists who want to work on projects involving image or text generation.
See salaries and explore the career path for Research Scientist
Computer Vision Engineer
Computer vision engineers design and develop systems that can see and understand the world around them. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for computer vision engineers who want to work on projects involving image or text generation.
See salaries and explore the career path for Computer Vision Engineer
Natural Language Processing Engineer
Natural language processing engineers design and develop systems that can understand and generate human language. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for text generation. This skill can be helpful for natural language processing engineers who want to work on projects involving image or text generation.
See salaries and explore the career path for Natural Language Processing Engineer
Artificial Intelligence Engineer
Artificial intelligence engineers design and develop systems that can think and learn like humans. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for artificial intelligence engineers who want to work on projects involving image or text generation.
See salaries and explore the career path for Artificial Intelligence Engineer
Robotics Engineer
Robotics engineers design, develop, and maintain robots. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for robotics engineers who want to work on projects involving image or text generation.
See salaries and explore the career path for Robotics Engineer
Game Developer
Game developers design and develop video games. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image generation. This skill can be helpful for game developers who want to work on projects involving image or text generation.
See salaries and explore the career path for Game Developer
Product Manager
Product managers are responsible for the development and launch of new products. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for product managers who want to work on projects involving image or text generation.
See salaries and explore the career path for Product Manager
Marketing Manager
Marketing managers are responsible for developing and executing marketing campaigns. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for marketing managers who want to work on projects involving image or text generation.
See salaries and explore the career path for Marketing Manager
Sales Manager
Sales managers are responsible for leading and motivating sales teams. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for sales managers who want to work on projects involving image or text generation.
See salaries and explore the career path for Sales Manager
Financial Analyst
Financial analysts provide advice to businesses and individuals on financial matters. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for financial analysts who want to work on projects involving image or text generation.
See salaries and explore the career path for Financial Analyst
Consultant
Consultants provide advice to businesses on a variety of topics, including strategy, operations, and technology. This course will teach you the basics of DCGANs, which are a type of neural network that can be used for image or text generation. This skill can be helpful for consultants who want to work on projects involving image or text generation.
See salaries and explore the career path for Consultant

Reading list

We've selected 11 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 : Generative Adversarial Network.
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Deep Learning
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Provides a comprehensive overview of the theory and practice of generative adversarial networks (GANs). It covers the basic principles of GANs, as well as more advanced topics such as training GANs, evaluating GANs, and using GANs for various applications.
Deep Learning (Adaptive Computation and Machine...
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$$$
Deep Learning (Adaptive Computation and Machine...
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Deep Learning with PyTorch
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Provides a practical introduction to deep learning with PyTorch. It covers the basics of PyTorch, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Deep Learning with PyTorch: Build, train, and tune...
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Deep Learning with PyTorch: Build, train, and tune...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Provides a practical introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers the basics of machine learning, as well as more advanced topics such as deep learning, natural language processing, and computer vision.
Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Cross-Lingual Word Embeddings
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Provides a comprehensive overview of deep learning for natural language processing. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and transformers.
Cross-Lingual Word Embeddings (Synthesis Lectures...
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Reinforcement Learning, second edition
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Provides a comprehensive overview of reinforcement learning. It covers the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning and multi-agent reinforcement learning.
Reinforcement Learning, second edition: An...
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Reinforcement Learning, second edition: An...
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Python Data Science Handbook
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Provides a comprehensive overview of unsupervised learning. It covers the basics of unsupervised learning, as well as more advanced topics such as clustering, dimensionality reduction, and generative models.
Python Data Science Handbook: Essential Tools for...
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Deep Learning for Coders with fastai and PyTorch
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Provides a practical introduction to deep learning with Fastai and PyTorch. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Deep Learning for Coders with Fastai and PyTorch:...
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Deep Learning for Coders with fastai and PyTorch:...
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Deep learning. Dalle basi alle architetture...
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Provides a practical introduction to deep learning from scratch. It covers the basics of deep learning, as well as more advanced topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Deep learning. Dalle basi alle architetture...
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Deep learning. Dalle basi alle architetture...
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Machine Learning for Hackers
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Provides a practical introduction to machine learning for hackers. It covers the basics of machine learning, as well as more advanced topics such as data wrangling, feature engineering, and model evaluation.
Machine Learning for Hackers: Case Studies and...
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Machine Learning for Hackers: Case Studies and...
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Machine Learning Engineering
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Provides a concise overview of machine learning. It covers the basics of machine learning, as well as more advanced topics such as deep learning and reinforcement learning.
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Machine Learning in Action
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Provides a practical introduction to machine learning. It covers the basics of machine learning, as well as more advanced topics such as data wrangling, feature engineering, and model evaluation.
Machine Learning in Action
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(中文) 机器学习实战 (图灵程序设计丛书 72) (Chinese Edition)
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Course image
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Focuses on implementing Deep Convolutional Generative Adversarial Networks (DCGANs) in PyTorch to develop handwritten digits
Builds a foundation in implementing and comprehending DCGAN architectures for real-world applications
Utilizes PyTorch, a respected deep learning framework, for implementing and training DCGANs
Provides practical knowledge and hands-on experience in building DCGANs for generating realistic handwritten digits
Suitable for learners familiar with convolutional neural networks and optimization algorithms
Course content focuses primarily on the practical aspects of DCGAN implementation, with less emphasis on theoretical concepts
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