You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. In this mega Ebook written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch. I live in Australia with my wife and son and love to write and code.

When technologists think of "supercomputer" they usually imagine government-owned computers like China's TaihuLight or public-private partnerships like the DOE-IBM's planned Summit. Private in-house supercomputers are much rarer. That makes Nvidia's new supercomputer, the DGX SaturnV, even more surprising. Not only has Nvidia revealed its own supercomputer, but the machine cracked TOP500's list of the 500 most powerful computers and took the number one spot as the world's most energy-efficient supercomputer. It relies on numerous Nvidia's 125 DGX-1s, the "AI supercomputer in a box" units built for deep learning.

DeepMind is a'vacuum for very, very clever people', says Ocado Technology's head of data

Abstract: The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem – unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) a'Watch, Listen, Attend and Spell' (WLAS) network that learns to transcribe videos of mouth motion to characters; (2) a curriculum learning strategy to accelerate training and to reduce overfitting; (3) a'Lip Reading Sentences' (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television. The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a significant margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that visual information helps to improve speech recognition performance even when the audio is available.

IBM ibm says it is trying to make it a little easier for its American workers to find the best cancer treatments. Beginning in January 2017, IBM employees in the U.S. will be able to use Watson supercomputer technology to help find the most effective oncology drugs and clinical trials for their specific cancers, IBM announced. "For anyone receiving the diagnosis, or supporting a loved one through it, cancer can be overwhelming," Kyu Rhee, MD, chief health officer, IBM Watson Health, said in the release, adding, "With this first-ever U.S. rollout of the technology, the full breadth and depth of Watson's services can benefit an entire population of individuals who need them." It's unclear just how much of IBM's workforce will receive the benefits (the firm has 377,000 employees worldwide, although it doesn't specify how many are in the U.S.) but the company says that many of the services will be covered by several of its American health plans. IBM's push into health care has been defined by its data-driven approach, especially when it comes to cancer.

Deeper neural nets often yield harder optimization problems. At the heart of deep learning lies a hard optimization problem. So hard that for several decades after the introduction of neural networks, the difficulty of optimization on deep neural networks was a barrier to their mainstream usage and contributed to their decline in the 1990s and 2000s. Since then, we have overcome this issue. In this post, I explore the "hardness" in optimizing neural networks and see what the theory has to say.

This month, Portugal is descended upon not by tourists in search of sunshine but by some estimated 55,000 tech-savvy individuals to attend an event quoted by Bloomberg as'Davos for Geeks'. I am of course talking about Web-Summit. For those unknown about Web-Summit, it's Europe's largest Technology Marketplace and invites companies within the tech industry to share their knowledge, and inspire through talks on how to solve some of the world's most pressing issues through new cutting edge systems and intelligent innovation. Whilst watching these talks one message resonated louder and clearer than all others – the future of technology is accelerating at an alarming rate, leaving all others who do not keep up the pace by the wayside. This message didn't, however, alarm me but rather triggered hope that we are advancing towards a technological level where we can tackle vital, real world issues such as: Climate change – our planet is warming causing problems such as rising sea levels and the first ever climate refugees.

Artificial Intelligence (AI) is the ability for a machine to solve problems by learning over time. It is similar to the way a brain processes information when making a decision. The term was coined by John McCarthy in 1956 who also quipped, "as soon as it works, no one calls it AI anymore." The technology is now so pervasive we no longer associate it with science fiction. From protecting you against credit card fraud, to recommending shows on Netflix and self-driving cars, this type of intelligence is spreading.

The Ishikawa Watanabe Laboratory, the University of Tokyo laboratory has all rights to the materials shown in the video. The paper "Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup" and its online demo is available here: http://hi.cs.waseda.ac.jp/ esimo/en/r... http://hi.cs.waseda.ac.jp:8081/ Recommended for you: Rocking Out With Convolutions - https://www.youtube.com/watch?v JKYQO... Separable Subsurface Scattering - https://www.youtube.com/watch?v 72_iA... WaveNet by Google DeepMind - https://www.youtube.com/watch?v CqFIV... WE WOULD LIKE TO THANK OUR GENEROUS PATREON SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: Sunil Kim, Julian Josephs, Daniel John Benton, Dave Rushton-Smith, Benjamin Kang. Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_c... Image credits: Bitmap and vector images (two of them): Wikipedia - https://en.wikipedia.org/wiki/Vector_... and https://en.wikipedia.org/wiki/Image_t... Image resolution: Wikipedia - https://en.wikipedia.org/wiki/Image_r... Vectorization: Wikipedia - https://en.wikipedia.org/wiki/Image_t... Thumbnail background - https://pixabay.com/photo-1281718/ Music: Dat Groove by Audionautix is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/...) Artist: http://audionautix.com/