Goto

Collaborating Authors

Results


A Comprehensive Guide to Swin Transformer

#artificialintelligence

Swin Transformer (Liu et al., 2021) is a transformer-based deep learning model with state-of-the-art performance in vision tasks. Unlike the Vision Transformer (ViT) (Dosovitskiy et al., 2020) which precedes it, Swin Transformer is highly efficient and has greater accuracy. Due to these desirable properties, Swin Transformers are used as the backbone in many vision-based model architectures today. Despite its wide adoption, I find that there is a lack of articles with detailed explanation in this topic. Therefore, this article aims to provide a comprehensive guide to Swin Transformers using illustrations and animations to help you better understand the concepts.


The "Hello World" of Tensorflow - KDnuggets

#artificialintelligence

Tensorflow is an open-source end-to-end machine learning framework that makes it easy to train and deploy the model. It consists of two words - tensor and flow. A tensor is a vector or a multidimensional array that is a standard way of representing the data in deep learning models. Flow implies how the data moves through a graph by undergoing the operations called nodes. It is used for numerical computation and large-scale machine learning by bundling various algorithms together.


Deep Learning For Compliance Checks: What's New? - KDnuggets

#artificialintelligence

Natural Language Processing (NLP) has long played a significant role in the compliance processes for major banks around the world. By implementing the different NLP techniques into the production processes, compliance departments can maintain detailed checks and keep up with regulator demands. All of these areas can benefit from document processing and the use of NLP techniques to get through the process more effectively. Certain verification tasks fall beyond the realm of using traditional, rules-based NLP systems. This is where deep learning can help fill these gaps, providing smoother and more efficient compliance checks. There are several challenges that make the rules-based system more complicated to use when undergoing check routines.


Data Science & Deep Learning for Business 20 Case Studies

#artificialintelligence

Welcome to the course on Data Science & Deep Learning for Business 20 Case Studies! This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade! "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! "It is pretty different in format, from others.


Editing a GAN's Latent Space With 'Blobs'

#artificialintelligence

New research from UC Berkeley and Adobe offers a way to directly edit the hyperreal content that can be created by a Generative Adversarial Network (GAN), but which can't usually be controlled, animated, or freely manipulated in a manner long familiar to Photoshop users and CGI practitioners. Titled BlobGAN, the method involves creating a grid of'blobs' – mathematical constructs that map directly to content within the latent space of the GAN. By moving the blobs, you can move the'objects' in a scene representation, in an intuitive manner that's nearer to CGI and CAD methods than many of the current attempts to map and control the GAN's latent space: Scene manipulation with BlobGAN: as the'blobs' are moved by the user, the disposition of latent objects and styles in the GAN are correspondingly altered. For more examples, see the paper's accompanying video, embedded at the end of this article, or at https://www.youtube.com/watch?v KpUv82VsU5k Since blobs correspond to'objects' in the scene mapped out in the GAN's latent space, all the objects are disentangled a priori, making it possible to alter them individually: Objects can be resized, shrunk, cloned, and removed, among other operations. Blobs can be duplicated in the interface, and their corresponding latent representations will also be'copied and pasted'.


Machine Learning Execution is a Directed Acyclic Graph

#artificialintelligence

As we continue to develop machine learning Operations (MLOps), we need to think of machine learning (ML) development and deployment flow as a Directed Acyclic Graph (DAG). DAG is a scary acronym, but so are LTSM, DNN, backward propagation, GAN, transformer, and many others. I think using "pipeline" is wrong. The problem with "pipeline" is that it is slang. I can assure you the human brain is not a "pipeline."


Complete Deep Learning In R With Keras & Others

#artificialintelligence

This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level! My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate.


10 Best AI Courses: Beginner to Advanced

#artificialintelligence

Are you looking for the Best Certification Courses for Artificial Intelligence?. If yes, then your search will end after reading this article. In this article, I will discuss the 10 Best Certification Courses for Artificial Intelligence. So, give your few minutes to this article and find out the Best AI Certification Course for you. Artificial Intelligence is changing our lives.


Machine Learning Requires Multiple Steps - EE Times Asia

#artificialintelligence

Deploying machine learning is a multi-step process. Deploying machine learning is a multi-step process. It involves selecting a model, training it for a specific task, validating it with test data, and then deploying and monitoring the model in production. Here, we'll discuss these steps and break them down to introduce you to machine learning. Machine learning refers to systems that, without explicit instruction, are capable of learning and improving.


100%OFF

#artificialintelligence

You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Data Science, Machine Learning, Python, R or Deep Learning, right? You've found the right Machine Learning course! Check out the table of contents below to see what all Machine Learning and Deep Learning models you are going to learn. How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.