Are you overwhelmed by overly-technical explanations of Deep Learning? If so, this series will bring you up to speed on this fast-growing field – without any...

Without a doubt, Bill Cunningham has an incredible ability for discerning clothing. One may wonder how he got that way. On top of being quite gifted, someone like Bill must have also taken notice of a lot of outfits throughout his 60-year career as a photographer. Assuming Bill works every day of the year (which isn't a bad approximation) and shoots 10 outfits an hour for 8 hours a day, that number is well over a million. Here's the motivating question: If we presented the same number of clothing images to an artificial neural network, can it learn to see the world of fashion like Bill Cunningham does? Said in a less sensationalized way, what we're proposing is training a neural network to recognize clothing from images and find us visually similar ones.

Its a constant that you use to normalize, right? And what comes after the normalizing constant in the equation is a vector, right? The authors are using Z' so that you know that the vector always gets normalized, you don't just calculate a constant at the start of training and reuse the same constant each time you calculate as the vector moves off normal.

There are also many good books that focus on one particular topic. For example, Sutton and Barto's Reinforcement Learning is a classic. And Yoshua Bengio's Deep Learning book (available online) is almost becoming a classic before it is published. But, you need a few of those books in order to build a somewhat comprehensive and balanced understanding of the field.

Each of those SMs also contains 32 FP64 CUDA cores, giving us the 1/2 rate for FP64 and new to the Pascal architecture is the ability to pack 2 FP16 operations inside a single FP32 CUDA core, when under the right conditions.Nvidia has just announced a new GPU platform called the Tesla P100. NVIDIA NVLink for maximum application scalability – The NVIDIA NVLink high-speed GPU interconnect scales applications across multiple GPUs, delivering a 5x acceleration in bandwidth compared to today's best-in-class solution.Specifically, the DGX-1 can pump out 170 teraflops – that's 170,000 floating operations per second – with its eight 16GB Tesla P100 graphics chips.The hype for the upcoming next generation NVIDIA GeForce Pascal graphics processing units is now at an all-time high as majority of the reports are claiming that the GPUs might be making their way into the market in the coming months. The GPU is meant for data centers, scientific and technical research, or churning statistics. It features NVIDIA's new Pascal GPU architecture, the latest memory and semiconductor process, and packaging technology – all to create the densest compute platform to date.Other technologies employed in the DGX-1 include 16nm FinFET fabrication technology, for improved energy efficiency; Chip on Wafer on Substrate with HBM2, for maximizing big data workloads; and new half-precision instructions to deliver more than 21 teraflops of peak performance for deep learning.Nvidia's new GPU is the first to be based on its Pascal architecture. It provides the throughput of 250 CPU-based servers, networking, cables and racks – all in a single box. This core is clocked at 1,328 MHz with boost clocks up to 1,480 MHz.

Graphics chipmaker Nvidia handily beat fourth quarter earnings targets Wednesday thanks to strong customer interest in its deep learning technology. The company reported a net income of 297 million, or 35 cents per share (statement). Non-GAAP earnings were 52 cents per share on a revenue of 1.4 billion, up 12 percent year-over-year. Wall Street was looking for earnings of 32 cents per share with 1.31 billion in revenue. For the year, Nvidia brought in 5.01 billion in revenue, a 7 percent increase from fiscal 2015, with earnings of 1.67 per share.

VC-backed artificial intelligence startups, which have seen a sevenfold increase in funding since 2010, have also seen corporations take a greater interest. While acquisitions of VC-backed AI startups by major corporations -- including Vocal IQ by Apple, Wit.ai by Facebook, and DeepMind by Google -- have been in the spotlight recently, corporate involvement in investing in these startups has also grown. Deals to AI startups involving corporates saw a 15x increase between 2010 and 2015. Just in the last quarter, companies including H2O.ai (Transamerica Ventures and Capital One Ventures), Gridspace (Wells Fargo Startup Accelerator) and Mobvoi (Google) saw backing from prominent corporates. Our artificial intelligence category covers startups primarily focused on developing AI, across areas including image processing, natural language processing, machine learning, deep learning, and predictive APIs, among other core applications.

Artificial Intelligence (AI) has four seasons: hype, disappointment, funding drought, and renewed interest. I've been involved in AI research for quite some time -- I became a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) in 1993 -- and I've weathered several seasonal cycles. What I'm seeing now, however, is the most puzzling cycle yet; either I'm getting old and addled, or the current cycle is unique in its magnitude. In these Big Data days, the big talk about AI's potential reminds me of what happened at the peak of earlier cycles (see, for example, the recent Wall Street Journal article. Once again, the focus is on a single technical component -- deep learning -- and hopes seem to be building that it can solve many very hard problems easily and more or less magically.

Appendix: Is there a simple algorithm for intelligence? If you benefit from the book, please make a small donation. I suggest 3, but you can choose the amount. Thanks to all the supporters who made the book possible, with especial thanks to Pavel Dudrenov. Neural Networks and Deep Learning is a free online book.

It's tempting to think of artificial intelligence (AI), cognitive computing and deep learning capabilities as somewhat futuristic--even with companies such as IBM, Microsoft and Google introducing increasingly sophisticated features. Yet machine learning--which constantly sorts through incoming data and improves on its own over time--is already making waves across a wide swath of industries, including travel, pharmaceutical research and financial services. Facebook and Google use machine learning to analyze users, click patterns and deliver personalized content and ads. Others are turning to machine learning and predictive analytics to understand everything from consumer buying and spending patterns to real estate and housing rental markets. Still others are putting it to use to improve cyber-security.