Generative Adversarial Networks Videos


What are GANs? By IBM Technology

What are GANs is a small introductory video on generative networks. Developed by IBM Tech and presented by Martin Keen, this video briefs you about the bifurcation of GANs into the generator and discriminatory models. You will be able to define a GAN and understand how it works after watching the video.

What are Generative Adversarial Networks?

This introductory YouTube video tutorial on GANs is a good place for beginners to learn what is meant by “generative,” “adversarial,” and “network.” it is posted by DigitalSreeni, a YouTube channel explaining several Python and AI-related topics. It is a short video wherein Sreeni will only brief you about the concept and give you an overview of how to implement it by providing code snippets.

The Math Behind Generative Adversarial Networks Clearly Explained! By Normalized Nerd

You will need a background in statistics and advanced-level mathematics, as the video begins with defining the generator and discriminator using probability concepts. The GAN video explains the computation behind a generative model in generating results and a discriminator model in predicting them. It talks less about theoretical concepts and is conversationally practical by showcasing all the formulas used.

Generative Adversarial Networks and TF-GAN by TensorFlow

This generative adversarial network video is a part of the Machine Learning Tech Talks hosted by TensorFlow. Research engineer Joel Shor talks about GANs as the recent development of machine learning technologies and an open-source library, TF-GAN, for training and evaluating GANs. Shor begins by describing GANs and their applications, then delves into the metrics.

Ian Goodfellow: Generative Adversarial Networks, NIPS 2016 Tutorial

This video session, delivered by Dr. Ian Goodfellow, is an insightful discussion for those without experience with generative adversarial networks. Dr. Goodfellow is the man behind this class of machine learning frameworks and aims to promote a greater audience to understand and utilize GANs to improve on other core algorithms. While watching the video, you will learn about the entire learning process of the adversarial game between the generator and the discriminator.

Conditional GANs and their applications by DigitalSreeni

The video talks about standard GANs and their usefulness in generating random images from the domain. You will learn that the standard GANs can be conditioned using specific image modalities and the methods that generate them. This conditioning is done by feeding the class labels in both adversarial models. The video also discusses applications like image-to-image translation, CycleGAN, super-resolution, and text-to-image synthesis.

Generative Adversarial Networks by Coursera

Coursera offers several courses on generative adversarial networks or GANs. These courses contain video lectures about fundamental concepts, applications, and challenges in learning and deploying GANs. The course “Build Basic Generative Adversarial Networks” is a part of the GAN specialization offered by Coursera that introduces you to the concept’s intuition and helps you build conditional GANs, training models using PyTorch and also covers the social implications of using such networks.

GANs and Autoencoders by Argonne Leadership Computing Facility

This generative adversarial network video features a session of ALCF AI for Science Training and introduces you to applying GANs and autoencoders in scientific research. Presented by Corey Adams, an assistant computer scientist at the Argonne Leadership Computing Facility, it is a detailed video discussing an ongoing ALCF research project, the study problem, the theoretical solution, and the codes.

Improved Consistency Regularization for GANs

This is one of those generative adversarial networks videos that uncovers a new technique to enhance consistency regularization, a model training technique invariant to data augmentations in semi-supervised learning. It discusses using the same regularization methods in unsupervised learning methods like SimCLR and FixMatch in the GAN.

Business Applications of GANs and Reinforcement Learning by Dataiku

If you want to learn about real-life applications of GANs, it is a great video posted by Dataiku. In this video, Alex Combessie, a data scientist at Dataiku, talks about the business applications of GAN AI technologies. GANs have succeeded in synthetic image generation, but can they be applied to forecast option prices? Combessie shares the story of two data scientists who deployed a GAN for option pricing.



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