courseraАнализ данных

Advanced Generative Adversarial Networks (GANs)

Курс от Packt

Продвинутый≈ 23 чАнглийский

О курсе

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Embark on an enlightening journey into the realm of Generative Adversarial Networks (GANs), where you will master the art of AI-driven image synthesis. This course begins with a solid foundation, introducing you to the basic concepts and components of GANs, such as the Generator and Discriminator. From there, you will delve into the intricacies of fully connected and deep convolutional GANs, understanding their architectures, and learning how to implement and optimize them effectively. The course progresses with hands-on tutorials using popular datasets like MNIST and CIFAR-10, where you will learn to load, preprocess, and train GAN models. Each step is meticulously explained, ensuring you gain practical knowledge and experience. By leveraging tools such as Google Colab, you will explore the capabilities of GPU acceleration, enhancing your model training efficiency and performance. As you advance, you will tackle more sophisticated topics, including Conditional GANs, label embedding, and model optimization techniques. The course culminates with practical projects where you apply your knowledge to generate and analyze realistic images, bridging the gap between theoretical concepts and real-world applications. This comprehensive approach ensures you emerge with the skills and confidence to harness the full potential of GANs in your projects. This course is designed for data scientists, machine learning engineers, and AI enthusiasts who have a basic understanding of neural networks and Python programming. Familiarity with deep learning frameworks like TensorFlow or Keras is recommended but not mandatory.

Навыки, которые вы освоите

Generative Adversarial Networks (GANs)Model Training Deep Learning Model Optimization Convolutional Neural Networks Image Analysis Embeddings Generative AI Network Architecture Generative Model Architectures Artificial Neural Networks Keras (Neural Network Library)Tensorflow Data Preprocessing Transfer Learning

Программа курса

42 модулей · 113 учебных материалов

01ResNet50 Transfer Learning Using Google Colab GPU - Training and Prediction4 материалов

ResNet50 Transfer Learning Using Google Colab GPU - Training and Prediction

Introduction to the Course 'Advanced Generative Adversarial Networks (GANs)'Чтение Full Specialization ResourcesЧтение ResNet50 Transfer Learning Using Google Colab GPU - Training and PredictionВидео Implementing Transfer Learning with ResNet 50DIALOGUE

Учитесь у экспертов

Packt - Course Instructors

Преподаватель курса

02Popular Neural Network Types2 материалов

Popular Neural Network Types

03Generative Adversarial Networks GAN Introduction3 материалов

Generative Adversarial Networks GAN Introduction

Generative Adversarial Networks GAN IntroductionВидео Assessment 1Задание Exploring GANs: Understanding Generative and Discriminative ModelsDIALOGUE

05Generator and Discriminator Mechanism Explained2 материалов

Generator and Discriminator Mechanism Explained

Generator and Discriminator Mechanism ExplainedВидео Understanding GAN Training DynamicsDIALOGUE

06A fully Connected Simple GAN Using MNIST Dataset - Introduction3 материалов

A fully Connected Simple GAN Using MNIST Dataset - Introduction

A Fully Connected Simple GAN Using MNIST Dataset - IntroductionВидео Assessment 2Задание Creating a Fully Connected GAN with the MNIST DatasetDIALOGUE

07Fully Connected GAN - Loading the Dataset2 материалов

Fully Connected GAN - Loading the Dataset

Fully Connected GAN - Loading the DatasetВидео Exploring MNIST Dataset with Jupyter NotebookDIALOGUE

08Fully Connected GAN - Defining the Generator Function3 материалов

Fully Connected GAN - Defining the Generator Function

Fully Connected GAN - Defining the Generator Function - Part 1Видео Fully Connected GAN - Defining the Generator Function - Part 2Видео Building a Simple GAN GeneratorDIALOGUE

10Fully Connected GAN - Combining Generator and Discriminator Models2 материалов

Fully Connected GAN - Combining Generator and Discriminator Models

Fully Connected GAN - Combining Generator and Discriminator ModelsВидео Combining Generator and Discriminator in a GANDIALOGUE

11Fully Connected GAN - Compiling Discriminator and Combined GAN Models2 материалов

Fully Connected GAN - Compiling Discriminator and Combined GAN Models

Fully Connected GAN - Compiling Discriminator and Combined GAN ModelsВидео Compiling Models with Optimizers in GANsDIALOGUE

13Fully Connected GAN - Generator Training2 материалов

Fully Connected GAN - Generator Training

Fully Connected GAN - Generator TrainingВидео Training a Composite GAN ModelDIALOGUE

14Fully Connected GAN - Saving Log at Each Interval2 материалов

Fully Connected GAN - Saving Log at Each Interval

Fully Connected GAN - Saving Log at Each IntervalВидео Implementing Interval-Based Logging in Training LoopsDIALOGUE

15Fully Connected GAN - Plot the Log at Intervals3 материалов

Fully Connected GAN - Plot the Log at Intervals

Fully Connected GAN - Plot the Log at IntervalsВидео Assessment 5Задание Visualizing Data with MatplotlibDIALOGUE

16Fully Connected GAN - Display Generated Images3 материалов

Fully Connected GAN - Display Generated Images

Fully Connected GAN - Display Generated Images - Part 1Видео Fully Connected GAN - Display Generated Images - Part 2Видео Displaying GAN Generated ImagesDIALOGUE

17Saving the Trained Generator for Later Use2 материалов

Saving the Trained Generator for Later Use

Saving the Trained Generator for Later UseВидео Saving and Serializing Models in Deep LearningDIALOGUE

18Generating Fake Images Using the Saved GAN Model3 материалов

Generating Fake Images Using the Saved GAN Model

Generating Fake Images Using the Saved GAN ModelВидео Assessment 6Задание Loading and Using Pretrained GANsDIALOGUE

19Fully Connected GAN Versus Deep Convoluted GAN2 материалов

Fully Connected GAN Versus Deep Convoluted GAN

Fully Connected GAN Versus Deep Convoluted GANВидео Comparing CNN and Fully Connected NetworksDIALOGUE

20Deep Convolutional GAN - Loading the MNIST Handwritten Digits Dataset2 материалов

Deep Convolutional GAN - Loading the MNIST Handwritten Digits Dataset

Deep Convolutional GAN - Loading the MNIST Handwritten Digits DatasetВидео Analyzing MNIST Dataset with PythonDIALOGUE

22Deep Convolutional GAN - Defining the Discriminator Function2 материалов

Deep Convolutional GAN - Defining the Discriminator Function

Deep Convolutional GAN - Defining the Discriminator FunctionВидео Understanding DCGAN Discriminator ImplementationDIALOGUE

23Deep Convolutional GAN - Combining and Compiling the Model2 материалов

Deep Convolutional GAN - Combining and Compiling the Model

Deep Convolutional GAN - Combining and Compiling the ModelВидео Combining and Compiling Deep Learning ModelsDIALOGUE

24Deep Convolutional GAN - Training the Model3 материалов

Deep Convolutional GAN - Training the Model

Deep Convolutional GAN - Training the ModelВидео Assessment 8Задание Training Deep Convolutional GANsDIALOGUE

25Deep Convolutional GAN - Training the Model Using Google Colab GPU2 материалов

Deep Convolutional GAN - Training the Model Using Google Colab GPU

Deep Convolutional GAN - Training the Model Using Google Colab GPUВидео Using Google Colab for Deep Learning TrainingDIALOGUE

26Deep Convolutional GAN - Loading the Fashion MNIST Dataset2 материалов

Deep Convolutional GAN - Loading the Fashion MNIST Dataset

Deep Convolutional GAN - Loading the Fashion MNIST DatasetВидео Applying a DCGAN to a New DatasetDIALOGUE

27Deep Convolutional GAN - Training the MNIST Fashion Model Using Google Colab GPU3 материалов

Deep Convolutional GAN - Training the MNIST Fashion Model Using Google Colab GPU

Deep Convolutional GAN - Training the MNIST Fashion Model Using Google Colab GPUВидео Assessment 9Задание Training GAN Models in a Cloud EnvironmentDIALOGUE

28Deep Convolutional GAN - Loading the CIFAR-10 Dataset and Defining the Generator3 материалов

Deep Convolutional GAN - Loading the CIFAR-10 Dataset and Defining the Generator

Deep Convolutional GAN - Loading the CIFAR-10 Dataset and Generator - Part 1Видео Loading the CIFAR-10 Dataset and Defining the Generator - part 2Видео Creating a DCGAN with the CIFAR-10 DatasetDIALOGUE

29Deep Convolutional GAN - Defining the Discriminator2 материалов

Deep Convolutional GAN - Defining the Discriminator

Deep Convolutional GAN - Defining the DiscriminatorВидео Understanding Convolutional Neural Networks for Image ClassificationDIALOGUE

30Deep Convolutional GAN CIFAR-10 - Training the Model3 материалов

Deep Convolutional GAN CIFAR-10 - Training the Model

Deep Convolutional GAN CIFAR-10 - Training the ModelВидео Assessment 10Задание Troubleshooting Model Training ErrorsDIALOGUE

31Deep Convolutional GAN - Training the CIFAR-10 Model Using Google Colab GPU2 материалов

Deep Convolutional GAN - Training the CIFAR-10 Model Using Google Colab GPU

Deep Convolutional GAN - Training the CIFAR-10 Model Using Google Colab GPUВидео Training and Managing Machine Learning Models in Google ColabDIALOGUE

32Vanilla GAN Versus Conditional GAN2 материалов

Vanilla GAN Versus Conditional GAN

Vanilla GAN Versus Conditional GANВидео Understanding Conditional GANs in Machine LearningDIALOGUE

33Conditional GAN - Defining the Basic Generator Function3 материалов

Conditional GAN - Defining the Basic Generator Function

Conditional GAN - Defining the Basic Generator FunctionВидео Assessment 11Задание Implementing a Conditional Generator with GANsDIALOGUE

34Conditional GAN - Label Embedding for Generator3 материалов

Conditional GAN - Label Embedding for Generator

Conditional GAN - Label Embedding for Generator - Part 1Видео Conditional GAN - Label Embedding for Generator - Part 2Видео Embedding Labels in Conditional GANsDIALOGUE

35Conditional GAN - Defining the Basic Discriminator Function2 материалов

Conditional GAN - Defining the Basic Discriminator Function

Conditional GAN - Defining the Basic Discriminator FunctionВидео Creating a Discriminator Model in GANsDIALOGUE

36Conditional GAN - Label Embedding for Discriminator3 материалов

Conditional GAN - Label Embedding for Discriminator

Conditional GAN - Label Embedding for DiscriminatorВидео Assessment 12Задание Embedding Conditional Labels in CGAN DiscriminatorDIALOGUE

37Conditional GAN - Combining and Compiling the Model2 материалов

Conditional GAN - Combining and Compiling the Model

Conditional GAN - Combining and Compiling the ModelВидео Combining and Compiling GAN ModelsDIALOGUE

39Conditional GAN - Display Generated Images3 материалов

Conditional GAN - Display Generated Images

Conditional GAN - Display Generated ImagesВидео Assessment 13Задание Creating and Displaying Labelled Sample ImagesDIALOGUE

40Conditional GAN - Training the MNIST Model Using Google Colab GPU2 материалов

Conditional GAN - Training the MNIST Model Using Google Colab GPU

Conditional GAN - Training the MNIST Model Using Google Colab GPUВидео Implementing and Customizing Conditional GANs in Google ColabDIALOGUE

41Conditional GAN - Training the Fashion MNIST Model Using Google Colab GPU3 материалов

Conditional GAN - Training the Fashion MNIST Model Using Google Colab GPU

Conditional GAN - Training the Fashion MNIST Model Using Google Colab GPUВидео Assessment 14Задание Using Conditional GANs for Fashion Image GenerationDIALOGUE