Launched in October 2017, Gluon is a new Open Source high-level API for Deep Learning developers. Here are ten reasons why you should take a good look at Gluon. Calling it documentation doesn't do it justice: Gluon actually comes with a full-fledged book on Deep Learning! Concepts, how to implement them from scratch, how to implement them with Gluon, pretty much all network architectures from perceptrons to Generative Adversial Networks… and a ton of notebooks. If you'd like to help him out, I'm sure he'd be happy to review your pull requests;) Gluon includes an extensive collection of pre-defined layers: from basic ones (Dense, Activation, Dropout, Embedding, etc.) to Convolution (2D, 3D, transposed) to Pooling (average, max and global max in 1D, 2D and 3D).

Everything is written on your skin. Every wrinkle, spot, and color tells a story, and not only a medical one. This miraculous organ can show you as a litmus paper whether you have a disease. For example, people with few red blood cells may look pale, while patients suffering from hepatitis have yellowish skin color. Yet, this is just the tip of the iceberg.

Drive.ai is a Silicon Valley startup founded by former lab mates out of Stanford University's Artificial Intelligence Lab. We are creating AI software for autonomous vehicles using deep learning, which we believe is the key to the future of transportation.

You will be able to program and build a vanilla Feedforward Neural Network (FNN) starting today via PyTorch. This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out. You are free to research more on that part. The output should be Python 3.6.3

Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions. However, unlike most optimization methods, Bayesian optimization typically does not use derivative information. In this paper we show how Bayesian optimization can exploit derivative information to decrease the number of objective function evaluations required for good performance. In particular, we develop a novel Bayesian optimization algorithm, the derivative-enabled knowledge-gradient (dKG), for which we show one-step Bayes-optimality, asymptotic consistency, and greater one-step value of information than is possible in the derivative-free setting. Our procedure accommodates noisy and incomplete derivative information, comes in both sequential and batch forms, and can optionally reduce the computational cost of inference through automatically selected retention of a single directional derivative. We also compute the d-KG acquisition function and its gradient using a novel fast discretization-free technique. We show d-KG provides state-of-the-art performance compared to a wide range of optimization procedures with and without gradients, on benchmarks including logistic regression, deep learning, kernel learning, and k-nearest neighbors.

Shipping deep learning models to production is a non-trivial task. If you don't believe me, take a second and look at the "tech giants" such as Amazon, Google, Microsoft, etc. -- nearly all of them provide some method to ship your machine learning/deep learning models to production in the cloud. Going with a model deployment service is perfectly fine and acceptable…but what if you wanted to own the entire process and not rely on external services? This type of situation is more common than you may think. How would you go about shipping your deep learning models to production in these situations, and perhaps most importantly, making it scalable at the same time?

A recent study from researchers at the Singapore National Eye Center (SNEC) shows just how proficient artificial intelligence (AI) is becoming at recognizing certain illnesses. In a study designed to test the performance of deep learning software, built to recognize and classify retinal images, the medical AI software proved to be reliable in recognizing three major eye diseases. The technology used machine learning to classify retinal images with or without diabetic retinopathy, glaucoma and age-related macular degeneration. According to Professor Wong Tien Yin, the study's lead and SNEC's medical director, "With the AI system, results (for the screening) should be instantaneous and it can potentially reduce 80 percent of the workload of graders and optometrists, freeing up their time for treatment." This begs the question of how the future of AI will impact the work that doctors do at every level, from diagnosis to treatment.

Product search is one of the key components in an online retail store. Essentially, you need a system that matches a text query with a set of products in your store. A good product search can understand user's query in any language, retrieve as many relevant products as possible, and finally present the result as a list, in which the preferred products should be at the top, and the irrelevant products should be at the bottom. Google web search), products are structured data. A product is often described by a list of key-value pairs, a set of pictures and some free text. In the developers' world, Apache Solr and Elasticsearch are known as de-facto solutions for full-text search, making them a top contender for building e-commerce product search. At the core, Solr/Elasticsearch is a symbolic information retrieval (IR) system.

In this guest article, Scot Schultz, Sr. Director AI/HPC and Technical Computing at Mellanox Technologies, explores how artificial intelligence is shaping up to launch the next industrial revolution. It has been said that artificial intelligence will create the next industrial revolution, the fourth industrial revolution that modern-day society has experienced since the dawn of mechanical production and steam power energy documented in 1784. Next on the timeline of society's pivotal transformation was electrical energy and mass production, while the third revolution because around 1969 with electronics and evolving to include the wide spread adoption of internet technologies. Today, many agree that the next wave of disruptive technology blurring the lines between the digital, physical and even the biological, will be the fourth industrial revolution of AI. The fusion of state-of-the-art computational capabilities, extensive automation and extreme connectivity is already impacting nearly every aspect of society, driving global economics and extending into every aspect of our daily life.

Artificial intelligence will continue to be buzzing in wealth management in 2018. But there's a short list of professionals who actually understand AI and can clearly explain how advisors and wealth management firms will benefit from it now and in the future. To help break it down, WealthMangement.com We asked Fritz to unpack AI in a way anyone in the industry can understand and even act on it. Prior to founding F2 Strategy, Fritz was the CTO for First Republic Private Wealth Management.