New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Human interaction with machines has experienced a great leap forward in recent years, largely driven by artificial intelligence (AI). From smart homes to self-driving cars, AI has become a seamless part of our daily lives. Voice interactions play a key role in many of these technological advances, most notably in language translation. Here, AI enables instant translation across a number of mediums: text, voice, images and even street signs. The technology works by recognizing individual words, then leveraging similarities in how various languages express the relationships between those words.
Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. The articles listed below represent a small fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Links to GitHub repos are provided when available. Especially relevant articles are marked with a "thumbs up" icon.
After going through a lot of theoretical articles on recurrent layers, I just wanted to build my first LSTM model and train it on some texts! But the huge list of exposed parameters for the layer and the delicacies of layer structures were too complicated for me. This meant I had to spend a lot of time going through StackOverflow and API definitions to get a clearer picture. This article is an attempt to consolidate all of the notes which can accelerate the process of transition from theory to practice. The goal of this guide is to develop a practical understanding of using recurrent layers like RNN and LSTM rather than to provide theoretical understanding.
Uday Kamath has more than 20 years of experience architecting and building analytics-based commercial solutions. He currently works as the Chief Analytics Officer at Digital Reasoning, one of the leading companies in AI for NLP and Speech Recognition, heading the Applied Machine Learning research group. Most recently, Uday served as the Chief Data Scientist at BAE Systems Applied Intelligence, building machine learning products and solutions for the financial industry, focused on fraud, compliance, and cybersecurity. Uday has previously authored many books on machine learning such as Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified and Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures. Uday has published many academic papers in different machine learning journals and conferences.
Artificial intelligence has arrived in our everyday lives--from search engines to self-driving cars. This has to do with the enormous computing power that has become available in recent years. But new results from AI research now show that simpler, smaller neural networks can be used to solve certain tasks even better, more efficiently, and more reliably than ever before. An international research team from TU Wien (Vienna), IST Austria and MIT (USA) has developed a new artificial intelligence system based on the brains of tiny animals, such as threadworms. This novel AI-system can control a vehicle with just a few artificial neurons.
This article will explain the concept to identify fake news. We use Deep Learning to classify a set of articles into'fake' and'real' news classes. The data set contains three CSV files which are train, test, and submit files. Now reset the index because we remove the nan values. Data pre-processing because the data have different characters, special characters, spaces and words that are not important. For that we can remove them with stopwords.
Bot Libre now allows you to create generic deep learning analytics and train them through our web API. Deep learning analytics can be used for a wide array of purposes to analyze and make predications on data. This example shows how to train a deep learning analytic to play checkers. You can use either the Bot Libre deep learning library, or the TensorFlow deep learning library. You can choose the inputs, outputs, and layers.
If you think that the Sahara is covered only by golden dunes and scorched rocks, you aren't alone. In an area of West Africa 30 times larger than Denmark, an international team, led by University of Copenhagen and NASA researchers, has counted over 1.8 billion trees and shrubs. The 1.3 million km2 area covers the western-most portion of the Sahara Desert, the Sahel and what are known as sub-humid zones of West Africa. "We were very surprised to see that quite a few trees actually grow in the Sahara Desert, because up until now, most people thought that virtually none existed. We counted hundreds of millions of trees in the desert alone. Doing so wouldn't have been possible without this technology. Indeed, I think it marks the beginning of a new scientific era," asserts Assistant Professor Martin Brandt of the University of Copenhagen's Department of Geosciences and Natural Resource Management, lead author of the study's scientific article, now published in Nature.
Deep Learning has been one of the most recent breakthroughs in AI Research. In this Session, we will learn about a few basics of Deep Learning, their applications and learn how to apply these concepts using H2O-3 via a hands-on session. This fast-paced session starts with a simple yet complete neural network, and builds on top of it adding functionality to and diving into different nuances of Neural Networks using a simple example with the H2O-3 API for a prediction problem. Speaker: Sanyam Bhutani is a Machine Learning Engineer and AI Content Creator at H2O.ai. He is also an inc42, Economic Times recognized Machine Learning Practitioner.