In any deep learning project, it's almost impossible to imagine an upper limit on the amount of data needed for training models and conducting analyses. "We need to get more data," said Patrick Lucey, director of data science at sports consulting company STATS LLC in Chicago. We want to reconstruct that story, [and] tell better stories, and we're limited because we can't get all the data we want." Deep learning, as defined by the use of multiple machine learning algorithms, such as neural networks strung together, isn't necessarily a new concept. However, it started to gain more widespread traction last year, as researchers and enterprises realized that analytical models could be turned loose on the massive troves of data businesses had accumulated since the dawn of the big data era. Deep learning algorithms require experience to sharpen their recommendations, and big data provides them with exactly the fuel they need. But this raises the question of when is enough data enough? Some of the most prominent deep learning examples used hundreds of thousands, even millions of records during the model training process. At STATS, Lucey has access to ample data, but said he still feels models could function better with more. The company maintains databases of game data going back to its beginnings in 1981. Its deepest data sets go back to 2010 with the NBA, and come from its SportVU system, a network of cameras installed at sports arenas that captures player movement data. This wealth of data has enabled Lucey and his team to do some interesting things with deep learning. For example, he and his team developed a model that looks at video data from NBA games and analyzes players' body positions to better define what an open shot looks like. Another STATS project applied deep learning algorithms to English Premier League soccer. STATS analyzed data beyond traditional statistics, like shots and goals, to understand the factors that led to longshot Leicester City Football Club taking home the title in the league's 2015-2016 season, which ended last May. The data science team at STATS primarily builds models in open source tools, such as the Google-created TensorFlow and scikit-learn, a library of machine learning models built in Python. These projects have been successful, according to Lucey. However, he added that he's already looking to sharpen analyses, and he thinks more data will help. In addition to larger data volumes, more detailed information will be necessary, he noted. Deep learning algorithms thrive on detailed data as much as large amounts of data, and that will play an important role as these models continue to improve and describe the world more accurately. "That's the key -- finding that context," Lucey said. "You can get a good prediction, but if it's washed over by context, it's not as valuable.

Artificial intelligence has begun seeping its way into every tech product and service. Now, companies are changing the underlying hardware to accommodate this shift. Apple is the latest company creating a dedicated AI processing chip to speed up the AI algorithms and save battery life on its devices, according to Bloomberg. The Bloomberg report said the chip is internally known as the Apple Neural Engine and will be used to assist devices for facial and speech recognition tasks. The latest iPhone 7 runs some of its AI tasks (mostly related to photographer) using the image signal processor and the graphics processing unit integrated on its A10 Fusion chip.

He is also the main author of H2O's Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C /MPI and had access to the world's largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators. Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker.

The day Thore Graepel joined Google's DeepMind artificial intelligence lab in the spring of 2015, his new colleagues sat him down for a game of Go. Over the previous year, they'd trained a neural network to play the ancient game. Graepel happened to be a player himself, holding a one dan rank, the Go equivalent of a black belt. As the game began with DeepMind researchers circled around him, Graepel was confident he would win. After all, he never had trouble playing other Go programs.

Cognitive computing represents the third era of computing. In the first era, (19th century) Charles Babbage, also known as'father of the computer' introduced the concept of a programmable computer. The second era (1950) experienced digital programming computers such as ENIAC and ushered an era of modern computing and programmable systems. And now to cognitive computing which works on deep learning algorithms and big data analytics to provide insights.

This week MapR announced a new solution called Quick Start Solution (QSS), focusing on deep learning applications. MapR touts QSS as a distributed deep learning (DL) product and services offering that enables the training of complex deep learning algorithms at scale. Here's the idea: deep learning requires lots of data, and it is complex. If MapR's Converged Data Platform is your data backbone, then QSS gives you what you need to use your data for DL applications. It makes sense, and it is in line with MapR's strategy.

Building a truly intelligent machine has been the holy grail of computer science since its very beginning. One of the first ideas of how to achieve this, born not long after the dawn of artificial intelligence itself, was to simulate a human brain. Modelling the network of brain neurons has led to as many disappointments as breakthroughs over its fascinating decades-long history. Today, with massive amounts of data and computational power easily available, this old idea, rebranded as "deep learning," is once again seducing thinkers, visionaries and practitioners all over the world, promising real artificial intelligence Artificial neural networks (ANNs) are a comparatively old family of artificial intelligence techniques, dating back to the 1950s. They are in fact one of the very first attempts at constructing an intelligent algorithm, which would be able to learn from experience without being explicitly told how to make decisions.

Artificial intelligence is where the competition is in IT, with Microsoft and Google both parading powerful, always-available AI tools for the enterprise at their respective developer conferences, Build and I/O, in May. It's not just about work: AI software can now play chess, go, and some retro video games better than any human -- and even drive a car better than many of us. These superhuman performances, albeit in narrow fields, are all possible thanks to the application of decades of AI research -- research that is increasingly, as at Build and I/O, making it out of the lab and into the real world. Alexa and Samsung Electronics' Bixby may offer less-than-superhuman performance, but they also require vastly less power than a supercomputer to run. Businesses can dabble on the edges of these, for example developing Alexa "skills" that allow Amazon Echo owners to interact with a company without having to dial its call center, or jump right in, using the various cloud-based speech recognition and text-to-speech "-as-a-service" offerings to develop full-fledged automated call centers of their own.

AMD has made plans to enter these markets with deep learning accelerators based on its Polaris and Vega architectures, but those chips haven't actually launched in-market yet. By all accounts, these are the killer growth markets for the industry as a whole, and they help explain why even some game developers like Blizzard want to get in on the AI craze. As compute resources shift towards Amazon, Microsoft, and other cloud service providers, the companies that can provide the hardware these workloads run on will be best positioned for the future. Smartphones and tablets didn't really work for Nvidia or Intel–making AMD's decision to stay out of those markets retrospectively look very, very wise–but both are positioned well to capitalize on these new dense server trends. AMD is obviously playing catch-up on the CPU and GPU front, but Ryzen should deliver strong server performance when Naples launches later this quarter.

In this series, I will try to give you an overview of the MXnet Deep Learning library: we'll look at its main features and its Python API (which I suspect will be the #1 choice). Later on, we'll explore some of the MXNet tutorials and notebooks available online, and we'll hopefully manage to understand every single line of code! If you'd like learn more about the rationale and the architecture of MXNet, you should read this paper, named "MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems". We'll cover most of the concepts presented in the paper, but hopefully in a more accessible way. First things first: let's install MXNet.