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Generative Adversarial Network study partners
25th April, 2021
Last date to join: 1st May, 2021
Hi, I have enrolled in Generative Adversarial Network Specialization. I am looking for a study partner with a good knowledge of PyTorch so as to gain maximum out of this specialization. I have already completed Deep Learning Specialization.
Novice
English
Description
About GANs Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more. About this Specialization The DeepLearning.AI 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. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. About you This Specialization is for software engineers, students, and researchers from any field, who are interested in machine learning and want to understand how GANs work. This Specialization provides an accessible pathway for all levels 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. Applied Learning Project Course 1: In this course, you will understand the fundamental components of GANs, build a basic GAN using PyTorch, use convolutional layers to build advanced DCGANs that processes images, apply W-Loss function to solve the vanishing gradient problem, and learn how to effectively control your GANs and build conditional GANs. Course 2: In this course, you will understand the challenges of evaluating GANs, compare different generative models, use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs, identify sources of bias and the ways to detect it in GANs, and learn and implement the techniques associated with the state-of-the-art StyleGAN. Course 3: In this course, you will use GANs for data augmentation and privacy preservation, survey more applications of GANs, and build Pix2Pix and CycleGAN for image translation. Read more
Generative Adversarial Networks (GANs) Specialization
Microcredentials 
Coursera
Free to Audit 
2 months at 10 hours a week 
Intermediate 
Paid Certificate Get notified about new study groups every week!
Generative Adversarial Network study partners
25th April, 2021
Last date to join: 1st May, 2021
Affiliate notice
Hi, I have enrolled in Generative Adversarial Network Specialization. I am looking for a study partner with a good knowledge of PyTorch so as to gain maximum out of this specialization. I have already completed Deep Learning Specialization.
Novice
English
-
TypeMicrocredentials
-
ProviderCoursera
-
PricingFree to Audit
-
Duration2 months at 10 hours a week
-
DifficultyIntermediate
-
CertificatePaid Certificate
About GANs
Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs.
Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more.
About this Specialization
The DeepLearning.AI 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. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.
About you
This Specialization is for software engineers, students, and researchers from any field, who are interested in machine learning and want to understand how GANs work.
This Specialization provides an accessible pathway for all levels 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.
Applied Learning Project
Course 1: In this course, you will understand the fundamental components of GANs, build a basic GAN using PyTorch, use convolutional layers to build advanced DCGANs that processes images, apply W-Loss function to solve the vanishing gradient problem, and learn how to effectively control your GANs and build conditional GANs.
Course 2: In this course, you will understand the challenges of evaluating GANs, compare different generative models, use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs, identify sources of bias and the ways to detect it in GANs, and learn and implement the techniques associated with the state-of-the-art StyleGAN.
Course 3: In this course, you will use GANs for data augmentation and privacy preservation, survey more applications of GANs, and build Pix2Pix and CycleGAN for image translation.
Read more
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1st May, 2021
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