Generative Adversarial Networks Powerpoint Ppt Template Bundles

Slide 1: This slide introduces Generative Adversarial Networks. State your company name and begin.
Slide 2: This slide highlights the different types of generative adversarial network models which include vanilla GAN, conditional GAN, deep convolutional GAN, etc.
Slide 3: This slide represents the use cases of generative adversarial networks which include text-to-image synthesis, anomaly detection, data augmentation, etc.
Slide 4: This slide highlights the key benefits of generative adversarial networks which include unsupervised learning without labelled data, learn complex data, etc.
Slide 5: This slide represents the generative adversarial network anatomy structure which include two main components which are generator and discriminator.
Slide 6: This slide describes the improved generative adversarial network variations which include progressive GANs, conditional GANs, image-to-image translation, etc.
Slide 7: This slide represents the 3D generative adversarial network’s generator and discriminator network architectures which include latent space and angle energy.
Slide 8: This slide outlines the roadmap for building vanilla generative adversarial networks from scratch and include import libraries, load and preprocess data, etc.
Slide 9: This slide gives an overview of working process of generative adversarial networks which include generator learns from training data, discriminator distinguishes attributes, etc.
Slide 10: This slide represents the best practices for deep convolutional generative adversarial networks which include apply batch normalization, initialize weights, etc.
Slide 11: This slide lists out the key mechanics of generative adversarial network in which GANs train until they reach convergence plateau,
Slide 12: This slide highlights the key components of generative adversarial networks which include generator, discriminator, loss function, training and evaluation.
Slide 13: This slide describes the training method for generative adversarial networks which include legitimate images, generative algorithm, discriminative algorithm, etc.
Slide 14: This slide represents the structure for conditional generative adversarial networks which include two major components that are discriminator and generator.
Slide 15: This slide depicts the sigmoid cross entropy loss function in generative adversarial networks using fake samples, real samples and sigmoid decision boundary.
Slide 16: This slide outlines the comparison between the architecture of traditional and style based generative adversarial networks.
Slide 17: This slide presents Music synthesis in generative adversarial networks icon.
Slide 18: This slide displays Image translation in generative adversarial network icon.
Slide 19: This slide presents Generative adversarial networks processing icon.
Slide 20: This slide shows all the icons included in the presentation.
Slide 21: This slide is titled as Additional Slides for moving forward.
Slide 22: This is an Idea Generation slide to state a new idea or highlight information, specifications etc.
Slide 23: This slide contains Puzzle with related icons and text.
Slide 24: This is a financial slide. Show your finance related stuff here.
Slide 25: This slide depicts Venn diagram with text boxes.
Slide 26: This is a Thank You slide with address, contact numbers and email address.