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What are GAN's actually- from underlying math to python code
What are GAN's actually- from underlying math to python code
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 2.02 GB
Genre: eLearning Video | Duration: 21 lectures (3 hour, 26 mins) | Language: English
Build Basic Generative Adversarial Networks (GANs)
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
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
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