New artist's biographies were created here in a sophisticated process, including fine-tuned GPT-2 on museum descriptions, and procedural name generation. That's why the outcomes were coherent, inspiring, and convincing. I guess, using GPT-3 one can even generate a museum's guide, combining authors to one conceptual exhibition, if you want. The art experience is not on-the-fly (using GANs in real-time is still a thing of the future nowadays), there are around 3000 artworks generated and randomly presented -- if you are here long enough, you may stumble upon already seen paintings. But the possibility to add new pieces is given -- the semi-eternity is possible.

Artificial intelligence (AI) and deep learning models can help advance research on neural degeneration, showing its capabilities in identifying and categorizing its forms on a model organism. Using the organism Caenorhabditis elegans or the roundworm – a 1-millimeter near-transparent nematode – researchers used deep learning to conduct a quantitative image-based analysis of neural degeneration patterns observed in the PVD neuron of the organism. Researchers from North Carolina State University have detailed their work in the journal BMC Biology, September 23. The worms were found alive last week in a biological container that was among the debris from the Space Shuttle Columbia recovered in East Texas. The worms are descendants of those that were part of an experiment that flew on Columbia's last mission before the spacecraft broke up on reentry February 1, killing all seven astronauts.

MIT CSAIL project shows that neural nets contain "subnetworks" 10x smaller that can just learn just as well - and often faster These days, nearly all AI-based products in our lives rely on "deep neural networks" that automatically learn to process labeled data. For most organizations and individuals, though, deep learning is tough to break into. To learn well, neural networks normally have to be quite large and need massive datasets. This training process usually requires multiple days of training and expensive graphics processing units (GPUs) - and sometimes even custom-designed hardware. But what if they don't actually have to be all that big after all?

General purpose artificial intelligence is increasingly becoming a game for Big Tech and Big Government in the US and China, leaving Europe trailing in the dust. But AI is also a game for nimble startups that can creatively apply the technology to particular business niches, giving Europe a chance to flourish. Those are two of the main takeaways from the third annual 176-slide State of AI report, written by Nathan Benaich and Ian Hogarth, two of the most active angel investors in the UK in AI startups. They also highlight the investment momentum that is building behind European AI startups in the life sciences and military fields, in particular. One of the most striking features of AI research, as shown in the report, is how it is being driven by huge amounts of capital, data and computing power, meaning that only the biggest players can lead in many fields.

We previously covered Charmed Labs PIXY2 computer vision camera based on an NXP LPC4330 microcontrollers that worked with Arduino, Raspberry Pi, and other development boards. The company is now back with a fully integrated more powerful solution with Vizy AI camera featuring a Raspberry Pi 4 SBC with up to 8GB RAM. The AI camera supports optional accessories including an M12 zoom lens with 87 FoV, an outdoor enclosure, lighting, a telescope adapter, and a 4G LTE cellular USB modem. Vizy can be configured via a web interface accessible from any device or computer with a web browser and programmed using Python with support for deep-learning neural networks in order to detect objects, people and/or animals using Tensorflow, OpenCV, or PyTorch. As an edge AI platform, all processing is done onboard, which means no connection to the cloud is needed nor a monthly fee has to be paid.

Free Coupon Discount - Machine Learning Practical: 6 Real-World Applications, Machine Learning - Get Your Hands Dirty by Solving Real Industry Challenges with Python Created by Kirill Eremenko, Hadelin de Ponteves, Dr. Ryan Ahmed, Ph.D., MBA, SuperDataScience Team, Rony Sulca Students also bought Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Deep Learning: GANs and Variational Autoencoders Artificial Intelligence: Reinforcement Learning in Python Natural Language Processing with Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Data Science: Natural Language Processing (NLP) in Python Preview this Udemy Course GET COUPON CODE Description So you know the theory of Machine Learning and know how to create your first algorithms. There are tons of courses out there about the underlying theory of Machine Learning which don't go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges? We gathered best industry professionals with tons of completed projects behind.

Reinforcement learning has seen a lot of progress in recent years. From DeepMind success with teaching machines how to play Atari games, then AlphaGo beating world champions in Go to recent OpenAI's progress on Dota 2, a multiplayer game where players divided into two teams compete with each other. The common thread is an artificial agent operating in a virtual world, where the prize is clear (e.g. On the other hand people are experimenting with AI agents operating in real-world. Each clip of Boston Dynamics gets a lot of press, showing robots performing amazing stunts, as you can see yourself here or here.

Introduction to Deep Learning with TensorFlow 2.0 Advanced implementation of regression model and essential tasks to be performed like feature selection in TensorFlow 2.x Bestseller What you'll learn In this course, you will learn advanced linear regression technique process and with this you can able to build any regression problem. With this intuition we will work on project: Customer Revenue Prediction. Problem Statement: A large child education toy company which sells educational tablets and gaming systems both online and in retail stores wanted to analyse the customer data. The goal of the problem is determine the following objective as shown below. Data Analysis & Preprocessing: Analyze customer data and draw the insights w.r.t revenue and based on the insights we will do data preprocessing.

The graph represents a network of 1,188 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 02 October 2020 at 10:24 UTC. The requested start date was Friday, 02 October 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 0-hour, 2-minute period from Tuesday, 29 September 2020 at 23:37 UTC to Thursday, 01 October 2020 at 23:40 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Amazon has created an open source toolkit for automated machine learning, called AutoGluon, designed to make it easier for software developers to take advantage of deep learning models in their applications. AutoGluon is intended for both machine learning experts and beginners, the company says. Officially launched January 9, AutoGluon lets developers harness machine learning models with image, text, or tabular data sets, sans any need to manually experiment. Developers can achieve strong, predictive performance in their applications. Accessible from the project website or GitHub, AutoGluon automates many decisions for developers, enabling them to produce a high-performance neural networking model with as few as three lines of code.