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Keep shoppingWhat are GAN's actually- from underlying math to python code
5,847 students
What are GAN's actually- from underlying math to python code
Build Basic Generative Adversarial Networks (GANs)
Created byRohan Aggarwal
Last updated 10/2020
English
What you'll learn
- GAN's Topic Overview and Prerequisites
- Theoretical Concept behind GAN's
- KL & JS Divergence
- Underlying math behind GAN's : Min - Max Game
- DCGAN & Hands on Python
- Conditional GAN & Hands on Python
- ACGAN & Hands on Python
- Challenges in training the GAN's
- Evaluation metrics & Tips for making GAN'S in real life
- Practical Application - Synthetic class specific image generation using GANs
- Some other cool applications of GAN's
- Semi-supervised learning with Generative Adversarial Networks
- Hands on Semi-supervised learning with Generative Adversarial Network
- Summary & additional resources
Explore related topics
Course content
6 sections • 21 lectures • 3h 26m total length
Requirements
- A thorough understanding of computer vision & Neural Networks concepts
- Good command on Python for data science
Description
In this course, you will:
- Learn about GANs and their applications
- Understand the intuition behind the fundamental components of GANs
- Explore and implement multiple GAN architectures
- Build conditional GANs & ACGAN's capable of generating examples from determined categories.
This Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach.
It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.
Build a comprehensive knowledge base and gain hands-on experience in GANs with the most loved language: Python.
Train your own model using Tensorflow & Keras, use it to create images, and evaluate a variety of advanced GANs.
This Specialization provides an accessible pathway for an intermediate level of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.
Who this course is for:
- Beginner Python developers curious for understanding GAN's, their underlying math, and getting your hands dirty with python.
- Beginner Python developers curious for some real life applications of GAN's