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Adversarial Generative Networks (GAN) has exploded the major top conferences, this course with the most easy-to-understand explanation to bring you a quick introduction to adversarial generative networks, you can also DIY a variety of generative data.

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Everyone Course Description

Adversarial Generative Networks have exploded in popularity in the last 16 years, and have become the talk of the town in deep learning circles. The course first explains the basic principles of Adversarial Generative Networks, and demonstrates the principles and processes through case studies. After the project combat adversarial generative network upgraded version of DCGAN, we can be based on DCGAN to generate any data you like.
The course code is based on the Tensorflow framework, and the case and project lessons will detail the usage of each line in the code through debugging.

Course Catalog

Chapter 1: Deep Learning Project - Adversarial Generative Networks (GAN) Course Introduction 05:21 Adversarial Generative Networks Image Explanation 07:17 Adversarial Generative Networks Working Principle 09:49 Case Study Adversarial Generative Networks: Environment Configuration 08:37 Case Study Adversarial Generative Networks: Constructing Discriminative Network Models 11:36 Case Study Adversarial Generative Networks: Constructing Generative Network Models 08. 08 Case Study Adversarial Generative Networks: Constructing Loss Functions 06:34 Case Study Adversarial Generative Networks: Training Adversarial Generative Networks 10:02 Chapter 2: Convolution-Based Adversarial Generative Networks (DCGAN) DCGAN Fundamentals 10:11 Network Modeling Architecture for DCGAN 06:09 DCGAN Project Practice: DIY the Data You Want to Generate 06:01 DCGAN project in action: configuration parameters 12:53 DCGAN project in action: convolution-based generative network architecture 11:51 DCGAN project in action: convolution-based discriminative network 10:00 DCGAN project in action: training DCGAN network 08:42 Chapter 3: adversarial generation of mnist datasets

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