Keras deep learning and generative adversarial networks (GAN)
Once we complete the fully connected GAN, we will then proceed with a more advanced Deep Convoluted GAN, or DCGAN. We will discuss what a DCGAN is and see the difference between a DCGAN and a fully connected GAN. Then we will try to implement the DCGAN. We will define the Generator function and defi...
| Format: | eBook |
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| Language: | English |
| Published: |
[Place of publication not identified]
Packt Publishing
2023
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| Edition: | [First edition] |
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | Once we complete the fully connected GAN, we will then proceed with a more advanced Deep Convoluted GAN, or DCGAN. We will discuss what a DCGAN is and see the difference between a DCGAN and a fully connected GAN. Then we will try to implement the DCGAN. We will define the Generator function and define the Discriminator function. By the end of the course, you will wield the skills to create, fine-tune, and deploy cutting-edge AI solutions, setting you apart in this evolving landscape. About The Author Abhilash Nelson: Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than eight years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design What You Will Learn Learn about Artificial Intelligence (AI) and machine learning Understand deep learning and neural networks Learn about lists, tuples, dictionaries, and functions in Python Learn Pandas, NumPy, and Matplotlib basics Explore the basic structure of artificial neurons and neural network Understand Stride, Padding, and Flattening concepts of CNNs Audience This course is designed for newcomers aiming to excel in deep learning and Generative Adversarial Networks (GANs) starting from the basics. Progress from novice to advanced through immersive learning. Suitable for roles like machine learning engineer, deep learning specialist, AI researcher, data scientist, and GAN developer. The course begins with the fundamentals of Python, encompassing concepts such as assignment, flow control, lists, tuples, dictionaries, and functions. We then move on to the Python NumPy library, which supports large arrays and matrices. Before embarking on the journey of deep learning, a comprehensive theoretical session awaits, expounding upon the essential structure of an artificial neuron and its amalgamation to form an artificial neural network. The exploration then delves into the realm of CNNs, text-based models, binary and multi-class classification, and the intricate world of image processing. The transformation continues with an in-depth exploration of the GAN paradigm, spanning from fundamental principles to advanced strategies. Attendees will have the opportunity to construct models, harness transfer learning techniques, and venture into the realm of conditional GANs. |
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| Item Description: | "Updated September 2023." |
| Physical Description: | 1 video file (17 hr., 26 min.) sound, color |
| ISBN: | 9781805125495 |