Multiview representation learning is very popular for latent factor analysis. It naturally arises in many data analysis, machine learning, and information retrieval applications to model dependent structures among multiple data sources. For computational convenience, existing approaches usually formulate the multiview representation learning as convex optimization problems, where global optima can be obtained by certain algorithms in polynomial time. However, many evidences have corroborated that heuristic nonconvex approaches also have good empirical computational performance and convergence to the global optima, although there is a lack of theoretical justification. Such a gap between theory and practice motivates us to study a nonconvex formulation for multiview representation learning, which can be efficiently solved by a simple stochastic gradient descent (SGD) algorithm. We first illustrate the geometry of the nonconvex formulation; Then by characterizing the dynamics of the approximate limiting process, we establish global rates of convergence to the global optima. Numerical experiments are provided to support our theory.

I have been actively focusing on specialising Deep Learning for the last 2 years. My personal interest towards Deep learning started around 2015 when Google open sourced Tensorflow. Tried quickly couple of examples from the Tensorflow documentation and left with a feeling that Deep learning is difficult, partly because the framework was new and required better hardware and tons of patience. Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software (ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India - Not still cheap), availability of data, good books and MOOCs. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera

Sure, there are currently all sorts of options for learning machine learning. You've got your more traditional methods like textbooks. You've got your fancy newfangled approaches like MOOCs and video lectures on YouTube. Podcasts, blogs, Quora questions (and sometimes answers), and research papers abound! But Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.

Similarity Based ML models and algorithms (KNN etc.)

This is an implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow. These are fast-to-evaluate and interpretable lattice models, also known as interpolated look-up tables. This library also provides a rich and intuitive set of regularizations and monotonicity constraints configurable per feature. This tutorial contains more detailed explanation about lattice models and usage in TensorFlow, and check out API docs for python APIs. TensorFlow Lattice is not an official Google product.

We use text mining a lot in day-to-day data mining operations. In order to share our knowledge on this, to show that R is an extremely mature platform to do business-oriented text analytics and to give you practical experience with text mining, our course on Text Mining with R is scheduled for the 3rd consecutive year at LStat, the Leuven Statistics Research Center (Belgium) as well as at the Data Science Academy in Brussels. Courses are scheduled 2 times in November 2017 and also in March 2018. This course is a hands-on course covering the use of text mining tools for the purpose of data analysis. It covers basic text handling, natural language engineering and statistical modelling on top of textual data.

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.

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