We are awash with text, from books, papers, blogs, tweets, news, and increasingly text from spoken utterances. Working with text is hard as it requires drawing upon knowledge from diverse domains such as linguistics, machine learning, statistical methods, and these days, deep learning. Deep learning methods are starting to out-compete the classical and statistical methods on some challenging natural language processing problems with singular and simpler models. In this crash course, you will discover how you can get started and confidently develop deep learning for natural language processing problems using Python in 7 days. This is a big and important post. You might want to bookmark it.

This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice, working files are provided along with the first lecture. This course contains lectures as videos along with the hands-on implementation of the concepts, additional assignments are also provided in the last section for your self-practice, working files are provided along with the first lecture.

Any plans on sharing the video lectures? By the way, my credits to the pace that Stanford starts courses on new topics. My faculty (EE) has yet to pick up on machine learning and deep learning. All the while, Stanford started CS231n on the Convnet wave and now it launches this course on the theory-of-deep-learning wave.

I discuss here off-the-beaten-path beautiful, even spectacular results from number theory: not just about prime numbers, but also about related problems such as integers that are sum of two squares. The connection between these numbers and prime numbers will appear later in this article. A few important unsolved mathematical conjectures are presented in a unified approach, and some new research material is also introduced, especially an attempt at generalizing and unifying concepts related to data set density and limiting distributions. The approach is very applied, focusing on algorithms, simulations, and big data, to help discover fascinating results. Even though some of the most exciting topics of mathematics are discussed here (including fundamental, century-old problems still unresolved as well as brand new hypotheses), most of the article can be understood by the layman. Among other things, you will learn some new ways to estimate Pi based on non-traditional experiments, or how a conjecture for prime numbers somehow generalizes to apply to Fibonacci numbers as well.

Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has for instance been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. This tutorial explains the theory of canonical correlation analysis including its regularised, kernel, and sparse variants. Additionally, the deep and Bayesian CCA extensions are briefly reviewed. Together with the numerical examples, this overview provides a coherent compendium on the applicability of the variants of canonical correlation analysis. By bringing together techniques for solving the optimisation problems, evaluating the statistical significance and generalisability of the canonical correlation model, and interpreting the relations, we hope that this article can serve as a hands-on tool for applying canonical correlation methods in data analysis.

Would you like to learn the mathematics behind machine learning? There aren't many resources out there that give simple detailed examples and that walk you through the topics step by step. If you're looking to gain a solid foundation in machine learning, allowing you to study on your own schedule at a fraction of the cost it would take at a traditional university, to further your career goals, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn machine learning for the first time, this online course is for you. Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary.

Cutting Edge Deep Learning For Coders, Part 2 from fast.ai

Deep generative models provide powerful tools for distributions over complicated manifolds, such as those of natural images. But many of these methods, including generative adversarial networks (GANs), can be difficult to train, in part because they are prone to mode collapse, which means that they characterize only a few modes of the true distribution. To address this, we introduce VEEGAN, which features a reconstructor network, reversing the action of the generator by mapping from data to noise. Our training objective retains the original asymptotic consistency guarantee of GANs, and can be interpreted as a novel autoencoder loss over the noise. In sharp contrast to a traditional autoencoder over data points, VEEGAN does not require specifying a loss function over the data, but rather only over the representations, which are standard normal by assumption. On an extensive set of synthetic and real world image datasets, VEEGAN indeed resists mode collapsing to a far greater extent than other recent GAN variants, and produces more realistic samples.

Recently, one of my friends and I were solving a practice problem. After 8 hours of hard work & coding, my friend Shubham got a score of 1153 (position 219). How did I get there? What if I tell you there exists a library called MLBox, which does most of the heavy lifting in machine learning for you in minimal lines of code? From missing value imputation to feature engineering using state-of-the-art Entity Embeddings for categorical features, MLBox has it all.

So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Want to know more about Carrie Anne? https://about.me/carrieannephilbin