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Janani Ravi

This course covers the important aspects of neural style transfer, a technique for transforming images, and discusses Generative Adversarial Networks in order to efficiently create realistic images and videos.

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This course covers the important aspects of neural style transfer, a technique for transforming images, and discusses Generative Adversarial Networks in order to efficiently create realistic images and videos.

Style transfer refers to the use of a neural network to transform an image so that it comes to artistically resemble another image, while still retaining its original content. Neural style transfer is fast becoming popular as a way to change the aesthetics of an image. In this course, Style Transfer with PyTorch, you will gain the ability to use pre-trained convolutional neural networks (CNNs) that come out-of-the-box in PyTorch for style transfer. First, you will learn how style transfer involves a style image as well as a content image, and a pretrained neural network that usually does not change at all during the training process. Next, you will discover how intermediate layers of the CNN are designated as style layers of interest and content layers of interest. Then, you will explore the minimization of two loss functions - a style loss and a content loss. Finally, you will delve into leveraging a new and much-hyped family of ML models, known as Generative Adversarial Networks (GANs) to create realistic images and videos. When you’re finished with this course, you will have the skills and knowledge to perform neural style transfer to get images that combine content and artistic style from two different inputs and use GANs to generate realistic images from noise.

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What's inside

Syllabus

Course Overview
Understanding Neural Style Transfer
Implementing Neural Style Transfer in PyTorch
Building Generative Adversarial Networks in PyTorch
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers concepts industry is using, such as neural style transfer, convolutional neural networks (CNNs), and generative adversarial networks (GANs)
Taught by instructors Janani Ravi, who teaches on neural networks, deep learning, and computer vision
Builds a strong foundation for learners new to neural style transfer and GANs
Teaches skills and knowledge used by professionals in artificial intelligence (AI), deep learning, and computer science (CS)
Exposes learners to new tools like neural style transfer and GANs which extend what they can do with AI, deep learning, and CS
Requires learners to come in with experience in Python and PyTorch since it does not teach them

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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 Style Transfer with PyTorch with these activities:
Organize course materials
Enhance organization and retention by compiling and reviewing course materials regularly.
Show steps
  • Create a system for organizing notes, assignments, and other resources
  • Review materials regularly to reinforce concepts
  • Summarize key points and create flashcards for easier memorization
Review the basics of computer vision
Review the fundamentals of computer vision, such as image acquisition, image processing, and feature extraction, to strengthen understanding of the foundational concepts used in this course.
Browse courses on Computer Vision
Show steps
  • Read introductory articles or textbooks on computer vision concepts.
  • Attend online lectures or tutorials on computer vision.
  • Complete hands-on exercises and practice problems related to image processing and feature extraction.
Participate in study groups or discussions
Engage with peers, exchange ideas, and gain different perspectives to enhance understanding.
Show steps
  • Join or form a study group with other students
  • Discuss course concepts, share insights, and work on assignments together
  • Attend online forums or discussion boards
  • Actively participate in discussions and ask questions
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Review Convolutional Neural Networks
Strengthen your foundational knowledge of convolutional neural networks (CNNs) by reviewing their architecture and functionality.
Show steps
  • Refer to course materials and online resources
  • Practice implementing basic CNNs in PyTorch
Review mathematics prerequisites
Review the essential mathematical concepts, like linear algebra, calculus, and probability, needed for neural style transfer and generative adversarial networks.
Browse courses on Linear Algebra
Show steps
  • Study textbooks or lecture notes on the relevant topics.
  • Solve practice problems to test your understanding.
  • Attend a refresher course or workshop on the topics.
Review a supplementary book
Strengthen foundational knowledge of deep learning, which is essential for understanding neural style transfer.
View Deep Learning on Amazon
Show steps
  • Read through chapters 1-3
  • Complete the exercises in chapter 2
Solve coding challenges
Sharpen coding skills and reinforce concepts related to neural style transfer.
Show steps
  • Solve coding challenges on platforms like LeetCode or CodeChef
  • Focus on problems related to deep learning, computer vision, and optimization
  • Review solutions and learn from different approaches
Explore tutorials on neural style transfer
Follow guided tutorials and walkthroughs to build a neural style transfer model from scratch. Experiment with different hyperparameters and explore the effects of various artistic styles on image transformation.
Browse courses on Neural Style Transfer
Show steps
  • Identify and install necessary software and libraries.
  • Follow step-by-step instructions to create a neural style transfer model.
  • Experiment with different artistic styles and content images.
Create Style Transfer Examples
Solidify your understanding of style transfer by creating your own examples using PyTorch.
Browse courses on Neural Style Transfer
Show steps
  • Select and gather images for style and content
  • Load and preprocess the images
  • Apply style transfer techniques
  • Evaluate and compare the results
Follow online tutorials
Supplement course content with additional learning resources and explore different perspectives.
Show steps
  • Identify reputable sources for online tutorials
  • Choose tutorials that focus on specific aspects of neural style transfer
  • Follow the instructions carefully and take notes
  • Experiment with different parameters and techniques
Practice Style Loss Minimization
Enhance your proficiency in style transfer by practicing the minimization of style loss using PyTorch.
Browse courses on Neural Style Transfer
Show steps
  • Set up a development environment with PyTorch
  • Load pre-trained models and images
  • Implement style loss calculation
  • Optimize the loss function
Attend a workshop on GANs and neural style transfer
Attend a hands-on workshop that delves into the principles and applications of GANs and neural style transfer. Learn from experts, network with fellow practitioners, and gain practical experience in using these techniques.
Show steps
  • Research and identify relevant workshops on GANs and neural style transfer.
  • Register and attend the workshop.
  • Actively participate in the workshop activities and discussions.
Attend Workshop on Generative Adversarial Networks
Expand your knowledge of Generative Adversarial Networks (GANs) by attending a workshop dedicated to their applications in image generation.
Show steps
  • Research and identify suitable workshops
  • Register and attend the workshop
  • Actively participate in discussions and hands-on exercises
Guided Tutorial: Implement Style Transfer
Reinforce your understanding of neural style transfer by following a guided tutorial that demonstrates the implementation in PyTorch.
Browse courses on Neural Style Transfer
Show steps
  • Identify a suitable tutorial resource
  • Set up the development environment
  • Follow the tutorial step-by-step
  • Experiment with different parameters and images
Implement a neural style transfer model
Solidify understanding and build practical skills in neural style transfer.
Show steps
  • Set up a Python environment
  • Download and install necessary libraries
  • Build a custom neural network model
  • Train the model on a dataset
  • Evaluate the model's performance
Create a portfolio of stylized images
Develop a portfolio of stylized images using neural style transfer techniques. Showcase the diversity of artistic styles and experiment with different image combinations to create visually appealing and unique artwork.
Browse courses on Neural Style Transfer
Show steps
  • Collect a variety of content images.
  • Experiment with various artistic styles and parameter settings.
  • Refine and select the best stylized images for your portfolio.
Collaborate on neural style transfer projects
Join a study group or online community to collaborate with peers on neural style transfer projects. Share ideas, discuss challenges, and provide feedback on each other's work to enhance understanding and learning.
Browse courses on Neural Style Transfer
Show steps
  • Join an online forum or discussion group dedicated to neural style transfer.
  • Initiate discussions or respond to existing threads on project ideas, approaches, and challenges.
  • Share your project progress and seek feedback from peers.
Create a presentation or tutorial
Reinforce learning by explaining concepts to others and practicing communication skills.
Show steps
  • Choose a specific topic within neural style transfer
  • Gather information and research the topic thoroughly
  • Organize the material into a clear and logical structure
  • Develop visual aids and examples to support the presentation
  • Practice delivering the presentation or tutorial
Develop a case study
Apply neural style transfer concepts to a real-world scenario and showcase understanding through a deliverable.
Show steps
  • Choose a specific application or industry
  • Gather data and conduct research
  • Design and implement a neural style transfer model
  • Evaluate the results and draw conclusions
  • Write a report or create a presentation summarizing the case study

Career center

Learners who complete Style Transfer with PyTorch will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work with data scientists to determine the best models to use for a given problem, and then they develop and implement those models. They also monitor the performance of the models and make adjustments as needed. This course can help you become a Machine Learning Engineer by teaching you the basics of neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of machine learning tasks, including image processing, natural language processing, and speech recognition.
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify trends and patterns. They then use this information to develop predictive models that can be used to make better decisions. This course can help you become a Data Scientist by teaching you the basics of neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of data science tasks, including image analysis, natural language processing, and fraud detection.
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision algorithms. These algorithms allow computers to see and understand the world around them. They are used in a variety of applications, including robotics, self-driving cars, and medical imaging. This course can help you become a Computer Vision Engineer by teaching you the basics of neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of computer vision tasks, including object detection, image segmentation, and facial recognition.
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with users to understand their needs and then develop software that meets those needs. They also test and debug software to ensure that it is working properly. This course can help you become a Software Engineer by teaching you the basics of neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of software development tasks, including image processing, natural language processing, and machine learning.
Research Scientist
Research Scientists conduct research to advance the understanding of science and technology. They work in a variety of fields, including computer science, physics, and biology. They typically have a PhD in their field of study. This course may be useful for Research Scientists who are working in the field of computer vision or machine learning. The course can help them to learn about the latest advances in neural style transfer and Generative Adversarial Networks.
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and implement artificial intelligence systems. These systems can perform a variety of tasks, including natural language processing, image recognition, and speech recognition. They are used in a variety of applications, including robotics, self-driving cars, and medical diagnosis. This course can help you become an Artificial Intelligence Engineer by teaching you the basics of neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of artificial intelligence tasks, including image generation, natural language processing, and machine learning.
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They then use this information to make recommendations to businesses on how to improve their operations. They typically have a bachelor's degree in a field such as statistics, computer science, or business. This course may be useful for Data Analysts who want to learn more about neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of data analysis tasks, including image analysis, natural language processing, and fraud detection.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They use this information to make investment recommendations. They typically have a master's degree in a field such as financial engineering, mathematics, or statistics. This course may be useful for Quantitative Analysts who want to learn more about neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of financial modeling tasks, including risk assessment, portfolio optimization, and trading strategies.
Machine Learning Researcher
Machine Learning Researchers develop new machine learning algorithms and techniques. They work in a variety of fields, including computer science, statistics, and mathematics. They typically have a PhD in their field of study. This course may be useful for Machine Learning Researchers who are working on developing new neural style transfer or Generative Adversarial Network algorithms.
Computer Scientist
Computer Scientists design, develop, and implement computer systems. They work in a variety of fields, including software engineering, computer hardware, and artificial intelligence. They typically have a bachelor's degree in computer science or a related field. This course may be useful for Computer Scientists who want to learn more about neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of computer science tasks, including image processing, natural language processing, and machine learning.
Statistician
Statisticians collect, analyze, and interpret data. They use this information to make informed decisions about a variety of topics, including public health, business, and finance. They typically have a master's degree in statistics or a related field. This course may be useful for Statisticians who want to learn more about neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of statistical tasks, including data analysis, forecasting, and risk assessment.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve business problems. They work in a variety of industries, including manufacturing, healthcare, and finance. They typically have a master's degree in operations research or a related field. This course may be useful for Operations Research Analysts who want to learn more about neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of operations research tasks, including supply chain management, scheduling, and resource allocation.
Financial Analyst
Financial Analysts analyze financial data to make investment recommendations. They work in a variety of industries, including banking, investment management, and insurance. They typically have a bachelor's degree in finance or a related field. This course may be useful for Financial Analysts who want to learn more about neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of financial analysis tasks, including risk assessment, portfolio optimization, and trading strategies.
Actuary
Actuaries use mathematical and statistical techniques to assess risk. They work in a variety of industries, including insurance, finance, and healthcare. They typically have a bachelor's degree in mathematics or a related field. This course may be useful for Actuaries who want to learn more about neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of actuarial tasks, including risk assessment, pricing, and reserving.
Business Analyst
Business Analysts analyze business processes to identify areas for improvement. They work in a variety of industries, including healthcare, finance, and manufacturing. They typically have a bachelor's degree in business administration or a related field. This course may be useful for Business Analysts who want to learn more about neural style transfer and Generative Adversarial Networks. This knowledge can be applied to a variety of business analysis tasks, including process mapping, data analysis, and system design.

Reading list

We've selected 13 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 Style Transfer with PyTorch.
Comprehensive introduction to GANs, covering their history, theory, and applications. It valuable resource for anyone who wants to learn more about this powerful generative model.
Comprehensive introduction to deep learning, covering its history, different approaches, and applications. It valuable resource for anyone who wants to learn more about this powerful machine learning technique.
Provides a practical introduction to PyTorch, a popular deep learning framework. It great resource for anyone who wants to learn how to use PyTorch for neural style transfer or other deep learning tasks.
Provides a comprehensive overview of machine learning from a probabilistic perspective, covering different approaches, algorithms, and applications. It great resource for anyone who wants to learn more about the probabilistic foundations of machine learning.
Provides a comprehensive overview of Bayesian reasoning and machine learning, covering different approaches, algorithms, and applications. It great resource for anyone who wants to learn more about these powerful machine learning techniques.
Provides a comprehensive overview of statistical learning, covering different approaches, algorithms, and applications. It great resource for anyone who wants to learn more about this field.
Provides a practical introduction to machine learning using popular Python libraries such as Scikit-learn, Keras, and TensorFlow. It great resource for anyone who wants to learn more about this field.
Provides a comprehensive overview of pattern recognition and machine learning, including different approaches, algorithms, and applications. It great resource for anyone who wants to learn more about these fields.
Provides a practical introduction to machine learning, covering different approaches, algorithms, and applications. It great resource for anyone who wants to learn more about this field.
Provides a comprehensive introduction to computer vision, including image processing, object detection, and image segmentation. It great resource for anyone who wants to learn more about the fundamentals of computer vision.
Provides a comprehensive introduction to reinforcement learning, covering different approaches, algorithms, and applications. It great resource for anyone who wants to learn more about this powerful machine learning technique.
Provides a comprehensive overview of the mathematics behind machine learning, including linear algebra, calculus, and probability theory. It great resource for anyone who wants to learn more about the mathematical foundations of machine learning.

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