Machine Heart reports
Author: Danjiang
Coursera has just launched a special course on GAN, which you might consider taking during this National Day holiday.
Generative Adversarial Network (GAN) is one of the most powerful machine learning models today, capable of generating realistic images, videos, and audio outputs. Applications based on GAN are extensive, such as defending against adversarial attacks and data anonymization to protect privacy, enhancing cybersecurity, and generating new images, colorizing black-and-white images, and improving image resolution, as well as converting 2D images to 3D.
With the enhancement of computational power, the popularity and functionality of GAN continue to improve, opening up many new directions: for example, generating large amounts of data for training models, enabling unsupervised models to produce clearer and more accurate output images, while also providing insights into adversarial learning, adversarial samples, and model robustness in related research fields.
Recently, DeepLearning.AI launched the “Generative Adversarial Network (GAN) Special Course”, which systematically introduces the theory and methods of using GAN to generate images. It also includes discussions on social impact topics such as machine learning bias and privacy protection.
This course is suitable for software engineers, students, and researchers who are interested in machine learning and wish to understand how GAN works. The course content is designed to be as accessible as possible, ensuring that participants truly understand GAN and learn how to use it.
However, before entering this course, learners should have knowledge of deep learning, convolutional neural networks, possess certain Python skills, and have experience with deep learning frameworks (TensorFlow, Keras, PyTorch), as well as proficiency in calculus, linear algebra, and statistics.
The special course is divided into three sections:
Course 1: Build Basic Generative Adversarial Networks (GANs)
This section covers the basics of GAN, constructing the simplest GAN model using PyTorch, building a DCGAN to process images using convolutional layers, addressing the gradient vanishing problem with loss functions, and learning how to control GAN and build conditional GAN.
Course 2: Build Better Generative Adversarial Networks (GANs)
This section will introduce the existing challenges of GAN models, compare different generative models, use Fréchet Inception Distance (FID) to evaluate the fidelity and diversity of GAN, identify sources of bias, methods to detect bias in GAN, and learn about StyleGAN-related techniques.
Course 3: Apply Generative Adversarial Networks (GANs)
This section will teach how to use GAN for data augmentation and privacy protection, familiarize with more types of GAN applications, and build Pix2Pix and CycleGAN for image transformation functions.
The instructor of this course is Sharon Zhou, a PhD student of Andrew Ng, whose research areas cover medicine, climate, and broader social welfare issues. Sharon Zhou graduated from Harvard University in 2015 with a joint degree in Classical Literature and Computer Science, and has held product manager positions in machine learning at companies like Google.
Like other special courses, this course also includes practical projects, which must be completed to finish the special course and obtain a certificate. If the special course includes separate practical project courses, all other courses must be completed before starting them. If you just want to read and view the course content, the course offers free auditing opportunities.
How to Match the Right Type of Database Based on Task Requirements?
In the white paper “Entering the Era of Dedicated Databases” released by AWS, eight types of databases are introduced: relational, key-value, document, in-memory, graph, time-series, ledger, and wide-column, analyzing the advantages, challenges, and main use cases of each type.
Clickto read the original textorscan the QR codeto apply for free access to the white paper.
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