Introduction to Generative Adversarial Networks with PyTorch
What you'll learn
- How Generative Adversarial Networks work internally
- How to implement state of the art GANs techniques and methods using PyTorch
- How to improve the training stability of GANs
Course content
- Preview07:58
Requirements
- Familiarity with Python Programming
- Familiarity with Deep Learning Concepts
Description
Master the basic building blocks of modern generative adversarial networks with a unique course that reviews the most recent research papers in GANs and at the same time gives the learner a very detailed hands-on experience in the topic. Start by learning the very basics of how GANs work and incrementally learn more cleverly crafted techniques that enhance your models from the basic GANs towards the more advanced Progressive Growing of GANs. On the journey, you shall learn a fair amount of deep learning concepts with an adequate discussion of the mathematics behind the modern models.
Who this course is for:
- Data scientists willing to take their skills to the next level in the area of GANs
- Research / Postgraduate Students willing to get a comprehensive overview of recent advancement made in the area of GANs
- Deep Learning practitioners willing to apply GANs at work in production environments
- Enthusiasts willing to stay up to date on GANs research and development
- Deep learning beginners willing to master the building blocks of modern GANs
Instructor
![Mustafa Qamaruddin](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.5 Instructor Rating
- 62 Reviews
- 49,413 Students
- 2 Courses
I'm a machine learning engineer with over 10 years of experience in the software development industry. I have been working with startups on solving problems in various domains; e-commerce applications, recommender systems, biometric identity control, and event management.
My main focus has been digital image processing, machine learning, and deep learning ever since the recent boom in artificial intelligence technologies. I'm backed by a strong foundation in academic topics such as probability, statistics, discrete mathematics, computational complexity, and numerical methods. I'm interested in learning new languages both natural and programming languages; I speak Arabic, English, German, and Spanish. Besides good command of C++ and Python, I'm also pushing the limits of Julia programming language.
I have been a machine learning engineer with over 10 years of experience in the software development industry, during which I have been working with many fast-growing startups solving problems in various domains.
Machine Learning Engineer using Python with TensorFlow, Keras, and PyTorch specialized in Computer Vision such as Classification, Detection, and Segmentation with over 10 years of experience in Software Development.
I do also have adequate exposure to deploying image processing and machine learning solutions developed in C++ to Android devices. I have also got hands-on experience working with CoreML and TensorFlow Lite for smart & intelligent mobile applications.
I have been involved in many successful software products and worked with many startups to design and implement digital solutions. I can fluently communicate in English, Spanish, and German.
My blog has many articles on different topics such as algorithms, machine learning, and software architecture design.