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SKU/Artículo: AMZ-B0DTG11Z88
Generative Adversarial Networks: 33 Comprehensively Commented Python Implementations of Generative Adversarial Networks(GANs) (Stochastic Sorcerers)
Format:
Hardcover
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.72 kg
0.72 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- An Invaluable Resource for Mastering Adversarial Deep LearningThis comprehensive work offers rigorous coverage of state-of-the-art Generative Adversarial Networks (GANs), backed by fully-documented Python code. With hands-on implementation details, it illuminates the practical techniques that distinguish groundbreaking research from everyday experimentation. Scholars, machine learning engineers, and data scientists will discover a wealth of advanced methods to push the boundaries of automated content creation, synthetic data generation, and AI-driven transformations. What You’ll LearnDevelop DCGAN for high-quality image generation and grasp the basics of convolutional architectures optimized for GAN training.Implement Pix2Pix and CycleGAN for image-to-image translation, bringing sketches to life or seamlessly converting one domain to another.Apply StyleGAN and Progressive Growing of GANs for stunning, high-resolution visuals and intuitive style manipulation.Leverage SRGAN to enhance image resolution and clarity, especially vital for technical or medical imaging applications.Build 3D-GAN models for volumetric object synthesis, essential in fields like 3D design, gaming, and robotics.Combine WaveGAN or T-CGAN methodologies for audio and time-series applications, extending GAN innovation beyond visual tasks.Adopt specialized frameworks (e.g., DeblurGAN, Inpainting GAN, EnlightenGAN) to address image deblurring, missing data completion, and low-light enhancement.With 33 dedicated complete Python algorithmic tutorials, this reference ensures both breadth and depth in advanced GAN research and applications. Whether you aim to sharpen your skills, propel your academic pursuits, or commercialize powerful generative models, you will find an unparalleled step-by-step roadmap in these pages.
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U$S 87,98
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