Solving inverse problems with iterative algorithms is popular, especially for large data. Due to time constraints, the number of possible iterations is usually limited, potentially affecting the achievable accuracy. Given an error one is willing to tolerate, an important question is whether it is possible to modify the original iterations to obtain faster convergence to a minimizer achieving the allowed error without increasing the computational cost of each iteration considerably. Relying on recent recovery techniques developed for settings in which the desired signal belongs to some low-dimensional set, we show that using a coarse estimate of this set may lead to faster convergence at the cost of an additional reconstruction error related to the accuracy of the set approximation. Our theory ties to recent advances in sparse recovery, compressed sensing, and deep learning. Particularly, it may provide a possible explanation to the successful approximation of the l1-minimization solution by neural networks with layers representing iterations, as practiced in the learned iterative shrinkage-thresholding algorithm (LISTA).

'Big' high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality-reduction step that inherently distorts the data structure. For the same purpose, clustering methods are also often used. These methods also introduce a bias, either by starting from the assumption of a particular geometric form of the clusters, or by using iterative schemes to enhance cluster contours, with uncontrollable consequences. The goal of data analysis should, however, be to encode and detect structural data features at all scales and densities simultaneously, without assuming a parametric form of data point distances, or modifying them. We propose a novel approach that directly encodes data point neighborhood similarities as a sparse graph. Our non-iterative framework permits a transparent interpretation of data, without altering the original data dimension and metric. Several natural and synthetic data applications demonstrate the efficacy of our novel approach.

We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimization-based attacks, we find defenses relying on this effect can be circumvented. For each of the three types of obfuscated gradients we discover, we describe characteristic behaviors of defenses exhibiting this effect and develop attack techniques to overcome it. In a case study, examining non-certified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 8 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely and 1 partially.

One of most excruciating pain points during Data Exploration and Preparation stage of an Analytics project are missing values. How do you deal with missing values - ignore or treat them? The answer would depend on the percentage of those missing values in the dataset, the variables affected by missing values, whether those missing values are a part of dependent or the independent variables, etc. Missing Value treatment becomes important since the data insights or the performance of your predictive model could be impacted if the missing values are not appropriately handled.The 2 tables above give different insights. The inference from the table on the left with the missing data indicates lower count for Android Mobile users and iOS Tablet users and higher Average Transaction Value compared to the inference from the right table with no missing data. The inference from the data with missing values could adversely impact business decisions.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Machine Learning as we know is a huge deal in today's world. With the help of Machine Learning Algorithms, your computer is allowed to get trained on data inputs and use statistical analysis in order to output values that fall within a specific range. Considering the popularity and reach of the subject we decided to create an entire course on Machine Learning which will involve 5 detailed projects namely: 1. We hope you like it. It is going to be a very detailed project where we will be importing a data set of over 80,000 games and use that information to train a Linear Regression Model and a Random Force Regressor to make predictions based off the information we have!

We share our new paper on Stochastic Gradient Descent on Random Mixtures (SGDRM), which is a simple way of protecting data under data breach threats. The key idea of SGDRM is simple: we run SGD algorithm on random mixtures of the training data points. With SGDRM, when a data breach occurs, only the random mixtures will be leaked, not the original dataset! The detailed mechanism of SGDRM is summarized in the figure. We experimentally observe that SGDRM performs pretty well on general deep learning tasks such as MNIST/CIFAR10/Skin-Lesion classification.

Many developers (including myself) have included learning machine learning in their new year resolutions for 2018. Even after blocking an hour everyday in the calendar, I am hardly able to make progress. The key reason for this is the confusion on where to start and how to get started. It is overwhelming for an average developer to get started with machine learning. There are many tutorials, MOOCs, free resources, and blogs covering this topic. But they are only adding to the confusion by making it look complex.

Time series forecasting is hardly a new problem in data science and statistics. The term is self-explanatory and has been on business analysts' agenda for decades now: The very first practices of time series analysis and forecasting trace back to the early 1920s.

We study data-driven methods for community detection in graphs. This estimation problem is typically formulated in terms of the spectrum of certain operators, as well as via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the Stochastic Block Model, recent research has unified these two approaches, and identified both statistical and computational signal-to-noise detection thresholds. We embed the resulting class of algorithms within a generic family of graph neural networks and show that they can reach those detection thresholds in a purely data-driven manner, without access to the underlying generative models and with no parameter assumptions. The resulting model is also tested on real datasets, requiring less computational steps and performing significantly better than rigid parametric models.