Generative Adversarial Networks Tutorial | What is GAN | Deep Learning for Beginners | Simplilearn
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In the video on "Generative adversarial Networks Tutorial", we will cover what is GAN, and understand Generators and Discriminators. In this deep learning for beginners tutorial, we will also experience a hands-on lab demo of how to use Generative Adversarial Networks with a celebrity face image dataset. Below are the topics covered in this tutorial:
00:00 Generative Adversarial Networks Tutorial
00:35 What is GAN?
01:10 What is Generator?
01:50 What is Discrimator?
03:40 Hands-on Lab Demo
What is Generative Adversarial Networks?
Generative Adversarial Networks (GANs) were introduced in 2014 by Ian J. Goodfellow and co-authors. GANs perform unsupervised learning tasks in machine learning. It consists of 2 models that automatically discover and learn the patterns in input data.
The two models are known as Generator and Discriminator.
They compete with each other to scrutinize, capture, and replicate the variations within a dataset. GANs can be used to generate new examples that plausibly could have been drawn from the original dataset.
What is a Generator?
A Generator in GANs is a neural network that creates fake data to be trained on the discriminator. It learns to generate plausible data. The generated examples/instances become negative training examples for the discriminator. It takes a fixed-length random vector carrying noise as input and generates a sample.
What is a Discriminator?
The Discriminator is a neural network that identifies real data from the fake data created by the Generator. The discriminator's training data comes from different two sources:
The real data instances, such as real pictures of birds, humans, currency notes, etc., are used by the Discriminator as positive samples during training.
The fake data instances created by the Generator are used as negative examples during the training process.
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In the video on “Generative adversarial Networks Tutorial”, we will cover what is GAN, and understand Generators and Discriminators. In this deep learning for beginners tutorial, we will also experience a hands-on lab demo of how to use Generative Adversarial Networks with a celebrity face image dataset. Below are the topics covered in this tutorial:
00:00 Generative Adversarial Networks Tutorial
00:35 What is GAN?
01:10 What is Generator?
01:50 What is Discrimator?
03:40 Hands-on Lab Demo
What is Generative Adversarial Networks?
Generative Adversarial Networks (GANs) were introduced in 2014 by Ian J. Goodfellow and co-authors. GANs perform unsupervised learning tasks in machine learning. It consists of 2 models that automatically discover and learn the patterns in input data.
The two models are known as Generator and Discriminator.
They compete with each other to scrutinize, capture, and replicate the variations within a dataset. GANs can be used to generate new examples that plausibly could have been drawn from the original dataset.
What is a Generator?
A Generator in GANs is a neural network that creates fake data to be trained on the discriminator. It learns to generate plausible data. The generated examples/instances become negative training examples for the discriminator. It takes a fixed-length random vector carrying noise as input and generates a sample.
What is a Discriminator?
The Discriminator is a neural network that identifies real data from the fake data created by the Generator. The discriminator’s training data comes from different two sources:
The real data instances, such as real pictures of birds, humans, currency notes, etc., are used by the Discriminator as positive samples during training.
The fake data instances created by the Generator are used as negative examples during the training process.
🔥 Free Machine Learning Course With Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=25March2023GenerativeAdversarialNetworksTutorial&utm_medium=Description&utm_source=youtube
✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check all the Machine Learning Projects Videos : https://youtube.com/playlist?list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
#GenerativeAdversarialNetworksTutorial #WhatisGAN #GAN #IntroductiontoGAN #DeepLearningWithPython #Pytorch #Python #DeepLearningPythonTutorial #NeuralNetworks #DeepLearning #AI #ArtificialIntelligence #MachineLearning #ML #2023 #ML2023 #MLTutorial #SimplilearnMachineLearning #Simplilearn
➡️ Professional Certificate Program In AI And Machine Learning
In collaboration with Purdue, our AI and ML Course will help you unlock your AI and ML potential. You’ll study Machine Learning, Deep Learning, Computer Vision, NLP, Speech Recognition, and Reinforcement Learning with Simplilearn’s AI ML course online.
✅ Key Features
– Professional Certificate Program certificate and Alumni Association membership
– Exclusive hackathons and Ask me Anything sessions by IBM
– 8X higher live interaction in live online classes by industry experts
– 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more
– Master Classes delivered by Purdue faculty and IBM experts
– Simplilearn’s JobAssist help
✅ Skills Covered
– Statistics
– Python
– Supervised Learning
– Unsupervised Learning
– NLP
– Neural Networks
– Computer Vision
-GANs
– Keras
– Tensorflow
– Reinforcement Learning
– Speech Recognition
– Recommendation Systems
– Ensemble Learning
– NumPy
🔥 Enroll Now: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=25March2023GenerativeAdversarialNetworksTutorial&utm_medium=Description&utm_source=youtube
🔥🔥 *Interested in Attending Live Classes? Call Us:* IN – 18002127688 / US – +18445327688
✅ Listen to what millions of users say about our courses: https://www.simplilearn.com/reviews?utm_campaign=25March2023GenerativeAdversarialNetworksTutorial&utm_medium=Description&utm_source=youtube
For more updates on courses and tips follow us on:
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Get the Android app: http://bit.ly/1WlVo4u
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