A recent acquisition looks to bring artificial intelligence and deep learning capabilities to the CRM powerhouse. Marketers using Salesforce products may soon find artificial intelligence–enabled features rolling out across the CRM platform due to the company's acquisition of AI and deep learning technology service MetaMind. MetaMind CEO Richard Socher announced the merger through a company blog post. The acquisition will ostensibly infuse Salesforce's existing services with MetaMind's natural language and deep machine learning software, which can reportedly analyze images, as well as text and sentiment. "At MetaMind, we've always been excited to bring breakthrough AI to high impact use cases. I can't think of a better place to have impact with AI than Salesforce and its many existing and future products," Socher said in a separate statement.

Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity. We prove a universal approximation theorem for the VGP, demonstrating its representative power for learning any model. For inference we present a variational objective inspired by auto-encoders and perform black box inference over a wide class of models. The VGP achieves new state-of-the-art results for unsupervised learning, inferring models such as the deep latent Gaussian model and the recently proposed DRAW.

One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is software engineers who do not have a machine learning or statistics background, but want to rapidly acquire one and begin using deep learning in their product or platform. Basically, if you are interested in reading this book and haven't been turned off by the content of this post, the book is likely for you. The book starts off covering the required background for understanding later material, along with historical context and elementary explanations of the technical concepts. In fact, the entire first part of the book is dedicated to building the technical foundation required to study deep learning.

Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification.

Now it's time for us to see how deep learning really works! In case you missed the previous part and is now wondering how deep learning has anything to do with you, go check it out! In this part, we'll show you all the basic concepts you need to get started with deep learning. Machine learning problems are typically where you want a computer to answer some questions without being explicitly programmed. For example, the question can be something like "What's the price of my 1800 sqft apartment in Seattle?", or "Is this news article telling the truth?"

Deep learning is a branch of machine learning that has shown incredible results on very difficult tasks like recognizing objects from an image, understanding speech and languages, and of course, playing board games. A bunch of smartest people have been working on it for decades, and it's absolutely state-of-the-art. Since you've clicked into a dummy's guide, chances are that you are a curious dummy. So first of all, let me answer a few questions I know a curious dummy might ask. It's software programs trying to mimic the human brain. The way it forms, the way it learns and the way it responds.

Time to get away from it all, enjoy our families, friends, and free time... and read up on the latest in data science, machine learning, and analytics. For those of us who can't completely disconnect, or are otherwise interested in reading up over the weekend, the following is a roundup of some of the best free recent ebooks and other online reading resources, as well as a classic throwback article worthy of the attention of newcomers to the field of machine learning. As reported earlier this week, the MIT Press Deep Learning book is finished, and the online version has been finalized. Written by deep learning heavyweights Ian Goodfellow, Yoshua Bengio, and Aaron Courville, the book is poised to become the deep learning book on the market. At over 700 pages, and being quite technical in content, this isn't a simple one-weekend read (at least, not for the majority of folks), but getting started this weekend means only a few more needed.

We've seen startling moves in artificial intelligence in 2015. Robots are doing the grunt work in factories. Driverless cars have become a reality. WiFi-enabled Barbie uses speech-recognition to talk (and listen) to children. Companies are using AI to improve their product and increase sales.

Microsoft is the world's most valuable company, with a 261 billion market cap. And an IBM computer named Deep Blue defeats Garry Kasparov, reigning world chess champion and, at the time, the highest-ranked chess player to have ever lived.

The days when a Facebook post would reach 90 percent of a page's audience have come and gone. In 2011, I was managing operations for Coca-Cola's global Facebook page, which had just surpassed Starbucks to become the largest CPG brand page on Facebook. The page was shared among dozens of markets because Facebook did not yet support regional pages. And each post was geotargeted to reach the right audience. One fine August day, Facebook dropped the targeting from a post specific to our Brazilian audience.