Generative Adversarial NetWorks (GANs) in Practice

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Posted on 21 Apr 21:40 | by AD-TEAM | 31 views

Generative Adversarial Networks (GANs) in Practice

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.70 GB | Duration: 7h 16m
With Introductory Review on Artificial Neural Networks and Deep Learning Algorithms and Models

What you'll learn
The fundamentals of Artificial Neural Networks (ANNs) and reviews state-of-the-art DL examples.
The fundamental of Deep learning and the most popular algorithms.
The most popular GAN algorithms features and requirements .
How to implement a GAN model in PRACTICE.
Several examples and applications of GAN.
Requirements
Probability,
Calculus,
Basic of Python, Tensor Flow, Keras, and Numpy.
Description
Deep learning is one of the most recent and advanced topics in machine learning, with several applications in many fields. It shows promising results in many areas, from computer vision to drug discovery and stock market prediction. There are many books and articles on deep learning that discuss its algorithms, theories, and applications. Also, because of its capabilities and potential in solving different problems by deploying different data types, many researchers and people who are not in computer science or related fields are interested in learning and using deep learning architectures in their projects.

This course gives you some fundamentals of artificial neural networks and deep learning and then has focused on Generative Adversarial networks and their applications with some coding examples to understand the concepts better. The course is suitable for people who are new in the machine learning field and deep learning and would like to learn how to implement deep learning algorithms (especially GAN algorithms) using python, TensorFlow, and Keras.

The course has seven chapters and starts with some fundamentals in machine learning concepts and end with some idea in CycleGAN. Each chapter has some quizzes and assignments to test students learning. It also provides the solution for each project.

I would expect this course's contents to be welcomed worldwide by undergraduate and graduate students and researchers in deep learning, including practitioners in academia and industry.

Who this course is for
It is useful for undergraduate and graduate students, as well as practitioners in industry and academia.
Anyone who would like to learn Neural networks, deep learning and GANs.

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