These curated articles will be a one stop solution for people who are getting started with Machine Learning or who already have. This article contains all the best articles of 2017 which gathered the interest of the Machine Learning community. Similar to the previous article on -"Best Deep Learning articles in 2017", I have added the used tool and the level of difficulty for each article to facilitate you with the choice. If you wish to include any other learning resource/article here, please mention them in the comments. A large amount of unstructured data present today is in the form of text, for example: Medical documents, legal agreements, tweets, blogs, newspapers, chat conversions etc.

Check out the tutorial session "Getting up and running with TensorFlow" at the AI Conference in New York City, April 29 to May 2, 2018. Bear with me: MNIST is where everyone in machine learning starts, but I hope this tutorial is different from the others out there. Back when TensorFlow was released to the public in November 2015, I remember following TensorFlow's beginner MNIST tutorial. I blindly copied and pasted all this code into my terminal and some numbers popped out as they should have. I thought, OK, I know there is something amazing happening here, why can I not see it? My goal was to make a MNIST tutorial that was both interactive and visual, and hopefully will teach you a thing or two that others just assume you know.

We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints. Sufficient conditions are provided, under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. We further extend the proposed algorithm to an asynchronous parallel variant with a near linear speedup. Numerical experiments demonstrate the efficiency of our algorithm in terms of both parameter estimation and computational performance.

Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and $K$-means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while $K$-means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using $K$-means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.

The estimation of the conditional mode, or modal regression [24, 11, 5, 22], is an important topic in statistics [21, 25, 24], econometrics [16, 17, 8, 15, 11], and machine learning [7, 22]. Compared to ordinary regression, modal regression is particularly useful when the data distribution is highly skewed and has fat tails. 1 In such a situation, ordinary regression, which estimates the conditional mean of the distribution, fails to capture the major trend underlying the data. This is because the conditional mean is not necessarily the point where the data points distribute densely, i.e., it can be far away from the majority of the data. Conditional mode is a convenient alternative to the conditional mean in this situation as it can capture the majority of the data. Hence, with modal regression, we can find a major trend underlying the data.

Quick Shift is a popular mode-seeking and clustering algorithm. We present finite sample statistical consistency guarantees for Quick Shift on mode and cluster recovery under mild distributional assumptions. We then apply our results to construct a consistent modal regression algorithm.

We derive a novel variational expectation maximization approach based on truncated variational distributions. Truncated distributions are proportional to exact posteriors within a subset of a discrete state space and equal zero otherwise. The novel variational approach is realized by first generalizing the standard variational EM framework to include variational distributions with exact (`hard') zeros. A fully variational treatment of truncated distributions then allows for deriving novel and mathematically grounded results, which in turn can be used to formulate novel efficient algorithms to optimize the parameters of probabilistic generative models. We find the free energies which correspond to truncated distributions to be given by concise and efficiently computable expressions, while update equations for model parameters (M-steps) remain in their standard form. Furthermore, we obtain generic expressions for expectation values w.r.t. truncated distributions. Based on these observations, we show how efficient and easily applicable meta-algorithms can be formulated that guarantee a monotonic increase of the free energy. Example applications of the here derived framework provide novel theoretical results and learning procedures for latent variable models as well as mixture models including procedures to tightly couple sampling and variational optimization approaches. Furthermore, by considering a special case of truncated variational distributions, we can cleanly and fully embed the well-known `hard EM' approaches into the variational EM framework, and we show that `hard EM' (for models with discrete latents) provably optimizes a lower free energy bound of the data log-likelihood.

Everyday, we depend on many systems and machines. We use a car to travel, a lift go up and down, and a plane to fly. Electricity comes through turbines and in a hospital machine keeps us alive. Some failures are an just an inconvenience, while others could mean life or death. When stakes are high, we perform regular maintenance on our systems.

Need help installing packages with pip? see the pip install tutorial The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved.

Applied machine learning is challenging. You must make many decisions where there is no known "right answer" for your specific problem, such as: This is challenging for beginners that expect that you can calculate or be told what data to use or how to best configure an algorithm. In this post, you will discover the intractable nature of designing learning systems and how to deal with it. This post is divided into 6 sections inspired by chapter 1 of Tom Mitchell's excellent 1997 book Machine Learning; they are: We can define a general learning task in the field of applied machine learning as a program that learns from experience on some task against a specific performance measure. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. We take this as a general definition for the types of learning tasks that we may be interested in for applied machine learning such as predictive modeling.