If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Booz Allen and NVIDIA are offering deep learning training. NVIDIA is working with Booz Allen Hamilton to rapidly build solutions that are needed in cyberdefense for both government and commercial customers. Now, certified Deep Learning Institute instructors from NVIDIA and Booz Allen are offering training to a variety of customers on how to build your own effective deep learning and data-driven solutions. 'Deep Learning Demystified,' hosted by Booz Allen and NVIDIA, will provide an introduction to deep learning, explore key fundamentals and opportunities, and how to best address current challenges. If you can't make our June 7th, 7:30AM - 11:00AM course, this course will also be offered over another two dates: "Together with NVIDIA, we've already seen through the Data Science Bowl how deep learning can speed cancer and heart disease diagnoses.
Over the past decade, advances in deep learning have transformed the fortunes of the artificial intelligence (AI) community. The neural network approach that researchers had largely written off by the end of the 1990s now seems likely to become the most widespread technology in machine learning. However, protagonists find it difficult to explain why deep learning often works well, but is prone to seemingly bizarre failures. The success of deep learning came with rapid improvements in computational power that came through the development of highly parallelized microprocessors and the discovery of ways to train networks with enormous numbers of virtual neurons assembled into tens of linked layers. Before these advances, neural networks were limited to simple structures that were easily outclassed in image and audio classification tasks by other machine-learning architectures such as support vector machines.
In this article I'll continue the discussion on Deep Learning with Apache Spark. You can see the first part here. In this part I will focus entirely on the DL pipelines library and how to use it from scratch. The continuous improvements on Apache Spark lead us to this discussion on how to do Deep Learning with it. I created a detailed timeline of the development of Apache Spark until now to see how we got here.
For good reason, deep learning is increasingly capturing mainstream attention. Just recently, on March 15th, Google DeepMind's AlphaGo AI -- technology based on deep neural networks -- beat Lee Sedol, one of the world's best Go players, in a professional Go match. Behind the scenes, deep learning is an active, fast-paced research area that's proliferating quickly among some of the world's most innovative companies. We are asked frequently about our favorite resources to get up to speed on deep learning and follow its rapid developments. As such, we've outlined below some of our favorite resources.
The tech world's obsession with artificial intelligence is driving companies to develop better, more optimized solutions for running machine learning and deep learning algorithms. The latest chips are not only making AI more available to various industries, they are also driving better efficiency and increased accuracy. When it comes to artificial intelligence (AI), 2018 is looking to be a year of significant growth. This is largely due to big steps being made in machine learning and deep learning. The deep learning market alone is expected to be worth US$1.7 billion by 2022, growing at a compound annual growth rate (CAGR) of 65.3 percent during the forecast period 2016 and 2022, according to a report by market research firm MarketsandMarkets.
One of the big mysteries of Deep Learning is, how do we apply this disruptive new AI technology to improving our businesses? There are plenty of questions that are quite open to be able to answer this question. Which business process shall I apply Deep Learning to? Is it even feasible to apply Deep Learning to my selected context? Will it be worth the effort?
From Facebook's research to DeepMind's legendary algorithms, deep learning has climbed its way to the top of the data science world. It has led to amazing innovations, incredible breakthroughs, and we are only just getting started! However if you are a newcomer to this field, the word "deep" might throw you into doubt. Deep learning is one of the hottest topics of this industry today, but it is unfortunately foreign and cryptic to most people. A lot of people carry an impression that deep learning involves a lot of mathematics and statistical knowledge. If you had similar questions about deep learning, but were not sure how, when and where to ask them – you are at the right place.
This is a tutorial of how to classify the Fashion-MNIST dataset with tf.keras, using a Convolutional Neural Network (CNN) architecture. In just a few lines of code, you can define and train a model that is able to classify the images with over 90% accuracy, even without much optimization. Fashion-MNIST can be used as drop-in replacement for the original MNIST dataset (10 categories of handwritten digits). It shares the same image size (28x28) and structure of training (60,000) and testing (10,000) splits. Keras is popular and well-regarded high-level deep learning API.
There's no doubt that the job market is changing. Gone are the days of learning how to do one job, sticking with it for 40 years and retiring with a desirable pension. In 2016, according to the U.S. Bureau of Labor Statistics, workers hold a job for an average of 4.2 years before moving on. And 35 percent of workplace skills in all industries are expected to change by 2020, according to the World Economic Forum. New technological developments continue to make certain roles in the workplace obsolete.
Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you give, the better it is – Sir Geoffrey Hinton (Google). The true challenge to Artificial Intelligence is to prove and solve the tasks that are easy for human to perform but hard to describe formally. Problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images. In deep learning this is the task we try to solve at AILabPage research. At the same time I also claim It is absolutely wrong to call Deep Learning as Machine Learning (in my opinion).