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Development Data Science Generative Adversarial Networks (GAN)

Introduction to Generative Adversarial Networks with PyTorch

A comprehensive course on GANs including state of the art methods, recent techniques, and step-by-step hands-on projects
Rating: 4.2 out of 54.2 (59 ratings)
478 students
Created by Mustafa Qamaruddin
Last updated 9/2020
English
English [Auto]
30-Day Money-Back Guarantee

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

8 sections • 33 lectures • 5h 53m total length

  • Preview07:58

  • Preview12:36
  • PyTorch Forward and Backward Propagation
    1 question
  • Preview07:27
  • PyTorch Autograd Mechanism
    1 question
  • PyTorch Custom Loss Function
    15:26
  • PyTorch Custom Loss Function
    1 question

  • Introduction to GANs
    04:51
  • Introduction to GANs
    1 question
  • Working of GAN Loss Function
    06:26
  • Working of GAN Loss Function
    1 question
  • Preview07:45
  • Implementing GAN Training Methodology
    1 question
  • Implement Vanilla GAN on MNIST Dataset to Generate Digits
    16:47
  • Implement Vanilla GAN on MNIST Dataset to Generate Digits
    1 question
  • [Coding Exercise] GAN Evaluation Metrics: Inception Score
    15:56
  • Preview15:50
  • GAN Evaluation Metrics
    4 questions

  • Introduction to Conditional GANs
    05:51
  • Introduction to Conditional GANs
    1 question
  • Implement Conditional GAN on MNIST Dataset
    28:01
  • Implement Conditional GAN on MNIST Dataset
    1 question
  • Working of Wasserstein Loss Function
    11:02
  • Working of Wasserstein Loss Function
    1 question
  • Implement Wasserstein Loss Function
    31:33
  • Implement Wasserstein Loss Function
    1 question
  • [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN
    16:42
  • [Coding Exercise] Gradient Penalty Wasserstein GAN - GP-WGAN
    1 question

  • Preview07:49
  • Introduction to DC-GANs
    1 question
  • Implement DC-GAN on UC Birds Dataset
    16:04
  • Implement DC-GAN on UC Birds Dataset
    1 question
  • Preview08:16
  • Working of Multi-way Loss Function
    1 question
  • Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset
    12:46
  • Implement multi-way loss with Auxiliary-GAN on UC Birds Dataset
    1 question

  • Introduction to Progressive GANs
    07:15
  • Introduction to Progressive GANs
    1 question
  • Implement Progressive GANs on Celebs Dataset
    32:30
  • Implement Progressive GANs on Celebs Dataset
    1 question
  • Hints, Tips, and Tricks for GAN Training
    04:39
  • Hints, Tips, and Tricks for GAN Training
    1 question

  • Introduction to U-NET Architecture
    07:47
  • Introduction to U-NET Architecture
    1 question
  • Working of Pix2Pix GAN and CycleGAN
    10:50
  • Working of Pix2Pix GAN and CycleGAN
    1 question
  • [Coding Exercise] Hands-on Pix2Pix GAN
    07:14
  • [Coding Exercise] Hands-on Pix2Pix GAN
    1 question
  • [Coding Exercise] Hands-on CycleGAN
    11:51
  • [Coding Exercise] Hands-on CycleGAN
    1 question
  • Working of Vid2Vid GAN
    06:05
  • Working of Vid2Vid GAN
    1 question
  • Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset
    04:30
  • Diving Deeper into Vid2Vid GAN using YouTube Dance Video Dataset
    1 question
  • Conclusion, Next Steps, and Future Directions
    02:46
  • Conclusion, Next Steps, and Future Directions
    1 question

  • Preview03:08
  • MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
    02:47
  • MGAN: Markovian Generative Adversarial Networks
    01:34
  • Preview01:52

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
Machine Learning Engineer
Mustafa Qamaruddin
  • 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.


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