Machine learning is eating the software world, and now deep learning is extending machine learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples.

This repository contains the NarrativeQA dataset. It includes the list of documents with Wikipedia summaries, links to full stories, and questions and answers. For a detailed description of this see the paper The NarrativeQA Reading Comprehension Challenge. Please cite the paper if you use this corpus in your work.

LG first laid the foundation for its AI efforts at CES 2017 when it announced DeepThinQ followed by the initiative to include Wi-Fi in its entire line of premium appliances launched this year. Advancing the company's innovations in AI, LG opened the Artificial Intelligence Lab in Seoul in June under company's CTO to tie together all its diverse AI research in technologies that recognize, deduce and learn from voice, video and sensors. LG's AI Lab has contributed to the development of the world's first space-learning air conditioner as well as intelligent refrigerators, washing machines and robot vacuum cleaners. "Our new ThinQ platform for LG's intelligent products is the latest way that LG is delivering innovations that make consumers' lives easier and more enjoyable," said David VanderWaal, vice president of marketing for LG Electronics USA. "LG ThinQ enables deep learning technology and connectivity across household products, delivering even greater capabilities and convenience." In the United States, LG already offers the most extensive range of Wi-Fi enabled appliances available* today featuring its LG SmartThinQ Wi-Fi connectivity.

Google's AI researchers recently showed off a new method for teaching computers to understand why some images are more aesthetically pleasing than others. Traditionally, machines sort images using basic categorization – like determining whether an image does or does not contain a cat. The new research demonstrates that AI can now rate image quality, regardless of category. The process, called neural image assessment (NIMA), uses deep learning to train a convolutional neural network (CNN) to predict ratings for images. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network … Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline.

In the spirit of Christmas, we're going to count down to the new year with the 12 Days of AI, bringing you a new, festive AI post every day! What better way to kick off than to look back at the RE•WORK highlights of 2017 and celebrate some of our successes of the past 12 months. This year saw RE•WORK hosting more events and bringing our globally renowned Summits to new locations. Our first ever Canadian Summit this year took place in Montreal, the'Silicon Valley of AI', and was one of our biggest events to date with over 600 attendees over the two days. We were fortunate enough to be joined by the'Godfathers of AI', Yoshua Bengio, Yann LeCun and Geoffrey Hinton who appeared on a panel together for the first time ever.

Tech giants love to tout how good their computers are at identifying what's depicted in a photograph. In 2015, deep learning algorithms designed by Google, Microsoft, and China's Baidu superseded humans at the task, at least initially. This week, Facebook announced that its facial-recognition technology is now smart enough to identify a photo of you, even if you're not tagged in it. But algorithms, unlike humans, are susceptible to a specific type of problem called an "adversarial example." These are specially designed optical illusions that fool computers into doing things like mistake a picture of a panda for one of a gibbon.

Artificial Intelligence has always been a concept present in pop culture, in movies and books. We've all heard of robots threatening human kind, or machines capable of taking over the world (Who isn't still haunted by Dolores's "Good Morning Daddy"). Now that artificial intelligence (AI) is increasingly becoming part of our daily lives, we can no longer disregard it as a concept far away from our reality. Few technologies have been able to reshape how we live and interact, or even have a disruptive potential in terms of work and increasing productivity. Studies estimate that implementing AI (calendar management, email, CRM, people analytics and office documents) by 2035, would increase labor productivity by 40%.

During this public lecture held at the Institute on December 12, Yann LeCun, Director of Facebook AI Research and Silver Professor of Computer Science at New York University, explored deep learning and the principles and methods for predictive...

This is the 3rd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I'll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on applying deep learning to Natural Language Processing. Natural language processing (NLP) is all about creating systems that process or "understand" language in order to perform certain tasks. The traditional approach to NLP involved a lot of domain knowledge of linguistics itself. Understanding terms such as phonemes and morphemes were pretty standard as there are whole linguistic classes dedicated to their study. Let's look at how traditional NLP would try to understand the following word. Let's say our goal is to gather some information about this word (characterize its sentiment, find its definition, etc).

Current AI is impressive, but it's not intelligent. After decades of little progress, the combination of big data and advances in computer hardware have brought AI applications to life: from self-driving cars to home assistants to augmented reality and instant language translation. If some of these applications feel like science fiction it's because deep learning algorithms are powering a true breakthrough in machine intelligence. But with these truly impressive advances comes a great deal of hype: fears of terminator-type bots turning on humans and stealing all our jobs. Check out http://www.vox.com to get up to speed on everything from Kurdistan to the Kim Kardashian app.