Coursera – Generative Adversarial Networks (GANs) Specialization 2024-7

Published on: 2024-07-02 10:50:29

Categories: 28

Description

Generative Adversarial Networks (GANs) Specialization is a training course for hostile Gan networks ( or GAN). GANs are powerful machine learning models capable of producing realistic images, videos and sounds . Even though this model of game theory originated in … but nowadays, the application is very wide, from improving cyber security and anonymous data to privacy, to produce artistic images, colourful images, black and White, increase the resolution, etc. making the Avatar, convert photo 2-dimensional to 3-dimensional and so many others have . This course will provide you with the production of images by GAN familiar and the level of your knowledge of the fundamental concepts to advanced techniques upgrade, ” and for engineers, software, etc. students and researchers in any field who are interested in machine learning, and understand the workings of the GAN are appropriate .

The course is divided into 3 sub-periods . In the first part of the. section of stem GAN upload you will understand, a GAN is simple with the use of the module, PyTorch want to build, layer design for the construction of DCGAN advanced that is able to process images and apply the function W-Loss are you will be using and how to build a GAN, the conditional will be familiar . The second part, to the challenges of evaluating GAN has allocated, and during it, how to compare the different models of GAN, using methods FID to assess the truth, and the diversity of models. detection bias resources and implement different techniques related to StyleGAN will be familiar . The last section is also dedicated to the practical use of GANs in enhancing data, privacy, making Pix2Pix and CycleGAN to translate images and other uses .

What do you learn

Understand Gans components, build simple Gans with PyTorch and advanced dcgans
Comparison of manufacturer models, use of Fréchet Inception Distance-FID method, diagnosis of ERBI and implementation of StyleGAN techniques
Use GANs to enhance data privacy mapping applications as well as test and build Pix2Pix and CycleGAN for image translation

What skills do you acquire

Generative Adversarial Networks-GANs )
Productive and interpreter photo to photo
Controlled and conditional production
WGANs, Dcgans and StyleGANs
ERBI in the GANs
And …

Specifications of Generative Adversarial Networks (GANs) Specialization

Publisher: Coursera
Lecturer: Sharon Zhou, Eda Zhou, Eric Zelikman
Language: English
Training level: Intermediate
Quantity: 3 courses
Duration of the course: with the proposed time of 9 hours per week, approximately 3 months

Courses

Build Basic Generative Adversarial Networks (GANs)
Build Better Generative Adversarial Networks (GANs)
Apply Generative Adversarial Networks (GANs)

Prerequisites

Learners should have a working knowledge of AI, deep learning, and convolutional neural networks. They should have intermediate Python skills as well as some experience with any deep learning framework (TensorFlow, Keras, or PyTorch). Learners should be proficient in basic calculus, linear algebra, and statistics.
We highly recommend that you complete the Deep Learning Specialization prior to starting the GANs Specialization.