Every day is Halloween for Virtual YouTubers or "VTubers" -- the new generation of wildly popular online entertainers whose voices and actions are represented in real time by colourful and expressive anime characters. Now, a Google researcher has released a deep neural network model that makes animating a VTube persona a little easier. Using motion capture systems to transfer human movements to cartoon characters in real-time is a process that can be traced back to the 90s. The approach however was not popularized, and the term "Virtual YouTuber" did not enter our vocabulary until the virtual character "Kizuna AI" debuted in 2016. Kizuna is a cute young girl with wide eyes and a pink butterfly bow perched atop her long flowing hair -- any otaku's dream.

Now, what if we wanted the neural network to tell us all four corners of the eye, or both eyes. If we call the points the first, the second, the third and fourth point, going from left to right, then we can modify the neural network to output \(l_{1x} \), \(l_{1y} \), for the first point, and \(l_{2x} \), \(l_{1y} \) for the second point and so on. The neural network can output the estimated position of all those four points of the person's face. What if we don't want just those four points? What if we want the output many points?

Solving the one-shot problem represents a challenge of face recognition task. This means that for most face recognition applications we need to recognize a person having only a single image or given just one example of that person's face. Typically, deep learning algorithms don't work well if there is only one training example. However, we will show how this problem can be tackled. Let's say that we have a database of \(4 \) pictures of employees in one organization. Let's say someone shows up at the office and we need to detect who has arrived.

Now, without waiting further, let's move on to the model building part. We will apply the augmentation techniques that are discussed in this article to generate images and then use those images to train the model. We will be working on the emergency vs non-emergency vehicle classification problem. You should be familiar with the problem statement if you've gone through my previous PyTorch articles. The aim of this project is to classify the images of vehicles as emergency or non-emergency. And you guessed it – it's an image classification problem.

Now, without waiting further, let's move on to the model building part. We will apply the augmentation techniques that are discussed in this article to generate images and then use those images to train the model. We will be working on the emergency vs non-emergency vehicle classification problem. You should be familiar with the problem statement if you've gone through my previous PyTorch articles. The aim of this project is to classify the images of vehicles as emergency or non-emergency. And you guessed it – it's an image classification problem.

Data Preparation for Machine learning 101: Why it's important and how to do it

It may seem like artificial intelligence is quickly seeping into just about everything. While that might raise concerns about a Skynet-style takeover, the quiet secret about AI is that it isn't taking over. In fact, some experts believe that AI in its current form is starting to slow down, reaching its maximum capacity -- at least for the time being. In an interview with Wired, Facebook's head of AI, Jerome Pesenti, theorized that the development of artificial intelligence and machine learning is about to "hit the wall." According to Pesenti, the deep learning mechanisms that currently help power and push the advancement of AI are pushing up against their limitations. Some of that has to do with the lack of necessary computing power to continue improving.

Imagine an APP for nuclei segmentation that is reliable and can be used by anyone in your lab. Recent advances in deep learning and artificial intelligence have enabled Visiopharm to develop novel tools for nuclei segmentation in brightfield images, providing you with the accuracy, precision and ease-of-use you have been waiting for. Whether you are an image analysis expert or a novice user, you can expect results quickly. This presentation will highlight Visiopharm's new AI Nuclei APP and how it can be combined with other AI and machine learning APPs to generate accurate and meaningful results. Can't make it to this webinar?

In this article, you'll find a collection of articles all about TensorFlow, which is "an end-to-end open source platform for machine learning." We have articles and tutorials for beginners who are just getting started with the basics, and we have articles for the pros who really want to dive deep into machine learning, deep learning, and TensorFlow. Before we begin, we'd like need to thank those who were a part of this article. DZone has and continues to be a community powered by contributors like you who are eager and passionate to share what they know with the rest of the world. In this article, take a look at TensorFlow 2.0 and explore major changes and noteworthy projects.

This article explores a seq2seq architecture for solving simple probability problems in Saxton et. A transformer is used to map questions to intermediate steps, while an external symbolic calculator evaluates intermediate expressions. This approach emulates how a student might solve math problems, by setting up intermediate equations, using a calculator to solve them, and using those results to construct further equations. A few months ago, DeepMind released Mathematics Dataset, a codebase for procedurally generating pairs of mathematics questions and answers, to serve as a benchmark for the ability of modern neural architectures to learn mathematical reasoning. The data consists of a wide variety of categories, ranging from basic arithmetic to probability. Both questions and answers are in the form of free-form text, making seq2seq models a natural first step for solving this dataset.