We consider multi-agent stochastic optimization problems over reproducing kernel Hilbert spaces (RKHS). In this setting, a network of interconnected agents aims to learn decision functions, i.e., nonlinear statistical models, that are optimal in terms of a global convex functional that aggregates data across the network, with only access to locally and sequentially observed samples. We propose solving this problem by allowing each agent to learn a local regression function while enforcing consensus constraints. We use a penalized variant of functional stochastic gradient descent operating simultaneously with low-dimensional subspace projections. These subspaces are constructed greedily by applying orthogonal matching pursuit to the sequence of kernel dictionaries and weights. By tuning the projection-induced bias, we propose an algorithm that allows for each individual agent to learn, based upon its locally observed data stream and message passing with its neighbors only, a regression function that is close to the globally optimal regression function. That is, we establish that with constant step-size selections agents' functions converge to a neighborhood of the globally optimal one while satisfying the consensus constraints as the penalty parameter is increased. Moreover, the complexity of the learned regression functions is guaranteed to remain finite. On both multi-class kernel logistic regression and multi-class kernel support vector classification with data generated from class-dependent Gaussian mixture models, we observe stable function estimation and state of the art performance for distributed online multi-class classification. Experiments on the Brodatz textures further substantiate the empirical validity of this approach.

Now that you have learnt how to manipulate data in the tutorials Basics & From Lab to Flow, you're ready to build a model to predict customer value. In this tutorial, you will create your first machine learning model by analyzing the historical customer records and order logs from Haiku T-Shirts. The goal of this tutorial is to predict whether a new customer will become a high-value customer, based on the information gathered during their first purchase. This tutorial assumes that you have completed Tutorial: From Lab to Flow prior to beginning this one! From Dataiku DSS home page, click on the Tutorials button in the left pane, and select Tutorial: Machine Learning. In the flow, you see the steps used in the previous tutorials to create, prepare, and join the customers and orders datasets.

Radiant is a robust tool for business analytics and running sophisticated models without any need for code development. It leverages the functions and tools in R and at the same time provides a user-friendly interface. With Radiant, you can manipulate and visualize your data, run different models from simple OLS to decision trees (CART) and neural networks, and evaluate your results. The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs.

The bag-of-words model is very simple to understand and implement and offers a lot of flexibility for customization on your specific text data. It has been used with great success on prediction problems like language modeling and documentation classification.

"This book appears in the Information Science and Statistics Series commissioned by the publishers.

In today's super competitive atmosphere, sales professionals need to move fast and work smart. Consider that half of sales teams have less than 50% of reps achieving quota. More companies are starting to use artificial intelligence (AI) to better empower sales teams. And it doesn't take an army of data scientists or petabytes of new data sources to incorporate AI into your sales arsenal. In our webinar'How AI Can Give Your Sales Team an Unfair Advantage', Dr. Anil Kaul, Co-Founder and CEO of Absolutdata, and Rajat Narang, Associate Director at Absolutdata share some practical examples of how AI is empowering sales teams to close more business.

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

You must understand the algorithms to get good (and be recognized as being good) at machine learning. In this mega Ebook is written in the friendly Machine Learning Mastery style that you're used to, finally cut through the math and learn exactly how machine learning algorithms work, then implement them from scratch, step-by-step. Click to jump straight to the packages. Jason's book is the best that exists to turn reasonably intelligent individuals with basic programming skills (any language) into sharp machine learning developers. I'm a developer and I feel like I don't really understand something until I can implement it from scratch. I need to understand each piece of it in order to understand the whole. The same thing applies to machine learning algorithms.

Reskilling is the buzzword in the IT sector. With the sector seeing huge churn due to automation and protectionism in the western markets, industry lobby group Nasscom's president R Chandrashekhar told employees in May: Re-skill or perish. The sector is seeing layoffs and voluntary severances. Companies' hiring is on the decline. One estimate even puts the likely job loss at a whopping 2 lakh over the next three years. And in that, the sector is class agnostic.

Ladies & gentleman, devs & ops, managers, minions, and everyone in the middle... welcome to round two of DZone's new Zone Leader Smackdown series! This time around our Zone Leaders will face off over the topic of AI and how it might affect the human job market in the upcoming Zone Leader Smackdown 2.0: Will AI Take Our Jobs or Enhance Them? The goal of the Smackdown is to give our Zone Leaders (ZLs) a debate platform in which they can discuss various dev-focused topics from opposing viewpoints, and you, the audience, get to choose our winner. This is a live debate format and will be presented as an interactive webinar with viewer polling throughout. Inspired by many different discussions and debates in our Zone Leader Slack channel, the Zone Leader Smackdown is an attempt to bring to life some of the magic that happens in the not-so-quiet comforts of the ZL Slack's GIF- and meme-filled chats.