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دانلود کتاب Generative Adversarial Networks with Python
دانلود کتاب شبکه های متخاصم مولد با پایتون
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Generative Adversarial Networks with Python
دسته بندی: الگوریتم ها و ساختارهای داده ها: شناخت الگو ویرایش: v1.5 نویسندگان: Jason Brownlee سری: Machine Learning Mastery ناشر: سال نشر: 2020 تعداد صفحات: 654 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 11 مگابایت
قیمت کتاب (تومان) : 49,000
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Copyright Contents Preface Introductions Welcome I Foundations What are Generative Adversarial Networks Overview What Are Generative Models? What Are Generative Adversarial Networks? Why Generative Adversarial Networks? Further Reading Summary How to Develop Deep Learning Models With Keras Tutorial Overview Keras Model Life-Cycle Keras Functional Models Standard Network Models Further Reading Summary How to Upsample with Convolutional Neural Networks Tutorial Overview Need for Upsampling in GANs How to Use the Upsampling Layer How to Use the Transpose Convolutional Layer Further Reading Summary How to Implement the GAN Training Algorithm Tutorial Overview How to Implement the GAN Training Algorithm Understanding the GAN Loss Function How to Train GAN Models in Practice Further Reading Summary How to Implement GAN Hacks to Train Stable Models Tutorial Overview Challenge of Training GANs Heuristics for Training Stable GANs Deep Convolutional GANs (DCGANs) Soumith Chintala's GAN Hacks Further Reading Summary II GAN Basics How to Develop a 1D GAN from Scratch Tutorial Overview Select a One-Dimensional Function Define a Discriminator Model Define a Generator Model Training the Generator Model Evaluating the Performance of the GAN Complete Example of Training the GAN Extensions Further Reading Summary How to Develop a DCGAN for Grayscale Handwritten Digits Tutorial Overview MNIST Handwritten Digit Dataset How to Define and Train the Discriminator Model How to Define and Use the Generator Model How to Train the Generator Model How to Evaluate GAN Model Performance Complete Example of GAN for MNIST How to Use the Final Generator Model Extensions Further Reading Summary How to Develop a DCGAN for Small Color Photographs Tutorial Overview CIFAR-10 Small Object Photograph Dataset How to Define and Train the Discriminator Model How to Define and Use the Generator Model How to Train the Generator Model How to Evaluate GAN Model Performance Complete Example of GAN for CIFAR-10 How to Use the Final Generator Model Extensions Further Reading Summary How to Explore the Latent Space When Generating Faces Tutorial Overview Vector Arithmetic in Latent Space Large-Scale CelebFaces Dataset (CelebA) How to Prepare the CelebA Faces Dataset How to Develop a GAN for CelebA How to Explore the Latent Space for Generated Faces Extensions Further Reading Summary How to Identify and Diagnose GAN Failure Modes Tutorial Overview How To Train a Stable GAN How To Identify a Mode Collapse How To Identify Convergence Failure Further Reading Summary III GAN Evaluation How to Evaluate Generative Adversarial Networks Overview Problem with Evaluating Generator Models Manual GAN Generator Evaluation Qualitative GAN Generator Evaluation Quantitative GAN Generator Evaluation Which GAN Evaluation Scheme to Use Further Reading Summary How to Implement the Inception Score Tutorial Overview What Is the Inception Score? How to Calculate the Inception Score How to Implement the Inception Score With NumPy How to Implement the Inception Score With Keras Problems With the Inception Score Further Reading Summary How to Implement the Frechet Inception Distance Tutorial Overview What Is the Frechet Inception Distance? How to Calculate the FID How to Implement the FID With NumPy How to Implement the FID With Keras How to Calculate the FID for Real Images Further Reading Summary IV GAN Loss How to Use Different GAN Loss Functions Overview Challenge of GAN Loss Standard GAN Loss Functions Alternate GAN Loss Functions Effect of Different GAN Loss Functions Further Reading Summary How to Develop a Least Squares GAN (LSGAN) Tutorial Overview What Is Least Squares GAN How to Develop an LSGAN for MNIST How to Generate Images With LSGAN Further Reading Summary How to Develop a Wasserstein GAN (WGAN) Tutorial Overview What Is a Wasserstein GAN? How to Implement Wasserstein Loss Wasserstein GAN Implementation Details How to Train a Wasserstein GAN Model How to Generate Images With WGAN Further Reading Summary V Conditional GANs How to Develop a Conditional GAN (cGAN) Tutorial Overview Conditional Generative Adversarial Networks Fashion-MNIST Clothing Photograph Dataset Unconditional GAN for Fashion-MNIST Conditional GAN for Fashion-MNIST Conditional Clothing Generation Extensions Further Reading Summary How to Develop an Information Maximizing GAN (InfoGAN) Tutorial Overview What Is the Information Maximizing GAN How to Implement the InfoGAN Loss Function How to Develop an InfoGAN for MNIST How to Use Control Codes With an InfoGAN Extensions Further Reading Summary How to Develop an Auxiliary Classifier GAN (AC-GAN) Tutorial Overview Auxiliary Classifier Generative Adversarial Networks Fashion-MNIST Clothing Photograph Dataset How to Define AC-GAN Models How to Develop an AC-GAN for Fashion-MNIST How to Generate Items of Clothing With the AC-GAN Extensions Further Reading Summary How to Develop a Semi-Supervised GAN (SGAN) Tutorial Overview What Is the Semi-Supervised GAN? How to Implement the Semi-Supervised Discriminator How to Develop a Semi-Supervised GAN for MNIST How to Use the Final SGAN Classifier Model Extensions Further Reading Summary VI Image Translation Introduction to Pix2Pix Overview The Problem of Image-to-Image Translation Pix2Pix GAN for Image-to-Image Translation Pix2Pix Architectural Details Applications of the Pix2Pix GAN Insight into Pix2Pix Architectural Choices Further Reading Summary How to Implement Pix2Pix Models Tutorial Overview What Is the Pix2Pix GAN? How to Implement the PatchGAN Discriminator Model How to Implement the U-Net Generator Model How to Implement Adversarial and L1 Loss How to Update Model Weights Further Reading Summary How to Develop a Pix2Pix End-to-End Tutorial Overview What Is the Pix2Pix GAN? Satellite to Map Image Translation Dataset How to Develop and Train a Pix2Pix Model How to Translate Images With a Pix2Pix Model How to Translate Google Maps to Satellite Images Extensions Further Reading Summary Introduction to the CycleGAN Overview Problem With Image-to-Image Translation Unpaired Image-to-Image Translation With CycleGAN What Is the CycleGAN Model Architecture Applications of CycleGAN Implementation Tips for CycleGAN Further Reading Summary How to Implement CycleGAN Models Tutorial Overview What Is the CycleGAN Architecture? How to Implement the CycleGAN Discriminator Model How to Implement the CycleGAN Generator Model How to Implement Composite Models and Loss How to Update Model Weights Further Reading Summary How to Develop the CycleGAN End-to-End Tutorial Overview What Is the CycleGAN? How to Prepare the Horses to Zebras Dataset How to Develop a CycleGAN to Translate Horse to Zebra How to Perform Image Translation with CycleGAN Extensions Further Reading Summary VII Advanced GANs Introduction to the BigGAN Overview Brittleness of GAN Training Develop Better GANs by Scaling Up How to Scale-Up GANs With BigGAN Example of Images Generated by BigGAN Further Reading Summary Introduction to the Progressive Growing GAN Overview GANs Are Generally Limited to Small Images Generate Large Images by Progressively Adding Layers How to Progressively Grow a GAN Images Generated by the Progressive Growing GAN How to Configure Progressive Growing GAN Models Further Reading Summary Introduction to the StyleGAN Overview Lacking Control Over Synthesized Images Control Style Using New Generator Model What Is the StyleGAN Model Architecture Examples of StyleGAN Generated Images Further Reading Summary VIII Appendix Getting Help Applied Neural Networks Programming Computer Vision Books Official Keras Destinations Where to Get Help with Keras How to Ask Questions Contact the Author How to Setup Python on Your Workstation Overview Download Anaconda Install Anaconda Start and Update Anaconda Install Deep Learning Libraries Further Reading Summary How to Setup Amazon EC2 for Deep Learning on GPUs Overview Setup Your AWS Account Launch Your Server Instance Login, Configure and Run Build and Run Models on AWS Close Your EC2 Instance Tips and Tricks for Using Keras on AWS Further Reading Summary IX Conclusions How Far You Have Come
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