Generate photorealistic human faces using GAN

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Course Description Learn to generate synthetic yet photorealistic human faces using GANBuild a strong foundation in GAN with this tutorial for intermediate developers. Understanding of  GANLearn DCGAN and CNN from scratch Learn Applications of GAN Learn to code a Generator network using Tensorflow Learn to code the Discriminator network using Tensorflow Learn to code training of DCGAN model User Jupyter Notebook for programming Build a realistic photo generator using DCGANA Powerful Skill at Your Fingertips  Learning the fundamentals of GAN puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation. No prior knowledge of CNN or GAN is assumed. I’ll be covering topics like CNN, Generator, GAN, and DCGAN from scratch. Jobs in the GAN area are plentiful, and being able to learn it with Python and Tensorflow will give you a strong edge. BERT is  state of art language model and surpasses all prior techniques in natural language processing. GAN is becoming very popular. Google, Yahoo, Bing and Youtube are few famous examples of GAN systems in action.  GAN engines are vital in synthetic image generation.  Learning GAN with Tensorflow will help you become a Computer Vision Machine learning engineer which is in high demand. Content and Overview  This course teaches you on how to build GAN using open source Tensorflow, Python, and Jupyter framework.  You will work along with me step by step to build following answers Introduction to GANBuild a jupyter notebook step by step using Tensorflow Build a synthetic human face generator What am I going to get from this course?Learn GAN and build a human face generator from a professional trainer from your own desk. Over 10 lectures teaching you how to build a human face generator Suitable for intermediate programmers and ideal for users who learn faster when shown. Visual training method, offering users increased retention and accelerated learning. Breaks even the most complex applications down into simplistic steps. Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.