More and more, human resources managers rely on data-driven algorithms to help with hiring decisions and to navigate a vast pool of potential job candidates. These software systems can in some cases be so efficient at screening resumes and evaluating personality tests that 72% of resumes are weeded out before a human ever sees them. But there are drawbacks to this level of efficiency. Man-made algorithms are fallible and may inadvertently reinforce discrimination in hiring practices. Any HR manager using such a system needs to be aware of its limitations and have a plan for dealing with them.

Earlier this year, Bill Gates, founder of Microsoft and the richest man on Earth, wrote an essay online at "The blog of Bill Gates," to college students graduating worldwide in 2017. One is artificial intelligence (AI). We have only begun to tap into all the ways it will make people's lives more productive and creative. The second is energy, because making it clean, affordable, and reliable will be essential for fighting poverty and climate change." The third field he mentioned was biosciences.

The Daily Roundup is our comprehensive coverage of the VR industry wrapped up into one daily email, delivered directly to your inbox. Education is an odd bird: we all know it could be better, while at the same time it is the best it has ever been in human history. For the last two centuries the world went through a great expansion in learning: our literacy rate skyrocketed from 12% to 88% worldwide, and Primary, Secondary and Tertiary education have all seen drastic growth (in schools and students), breaking records on almost a yearly basis. Lucas Rizzotto is an award-winning XR creator, industry speaker, and entrepreneur working on the the realities to come. You can follow his creations and thoughts on Facebook, Twitter, Medium or Instagram.

Sir Anthony Shelden, vice chancellor of the University of Buckingham and former master of Wellington College, predicts the change will happen within the next 10 years and will completely transform the education system. Teachers will remain in classrooms to set up equipment and maintain discipline according to Sir Anthony, but they will simply be assistants while the real education is done by artificial intelligence. "It certainly will change human life as we know it. It will open up the possibility of an Eton or Wellington education for all," said Sir Anthony. "Everyone can have the very best teacher and it's completely personalised; the software you're working with will be with you throughout your education journey. "It can move at the speed of the learner.

Every time students take a writing exercise on Quill.org–a Algorithms take account of every false word they type, every misplaced comma, every inappropriate conjunction, deepening a sense of where the nation's kids are succeeding in sentence-construction and where they need extra help. Instead of teachers having to correct errors late at night with a red pen, the system does it automatically, suggesting corrections and concepts on its own. The goal, says Peter Gault, who founded Quill three years ago, is to reach more students than traditional teaching methods, including those who need support the most. About 400,000 students in 2,000 schools have used the (mostly free) writing-instruction platform so far.

At this time, I knew nothing about backpropagation, and was completely ignorant about the differences between a Feedforward, Con-volutional and a Recurrent Neural Network. As I navigated through the humongous amount of data available on deep learning online, I found myself quite frustrated when it came to really understand what deep learning is, and not just applying it with some available library . In particular, the backpropagation update rules are seldom derived, and never in index form. Unfortunately for me, I have an "index" mind: seeing a 4 Dimensional convolution formula in matrix form does not do it for me. Since I am also stupid enough to like recoding the wheel in low level programming languages, the matrix form cannot be directly converted into working code either. I therefore started some notes for my personal use, where I tried to rederive everything from scratch in index form. I did so for the vanilla Feedforward network, then learned about L1 and L2 regularization, dropout[1], batch normalization[2], several gradient descent optimization techniques... Then turned to convolutional networks, from conventional single digit number of layer conv-pool architectures[3] to recent VGG[4] ResNet[5] ones, from local contrast normalization and rectification to bacthnorm... And finally I studied Recurrent Neural Network structures[6], from the standard formulation to the most recent LSTM one[7]. As my work progressed, my notes got bigger and bigger, until a point when I realized I might have enough material to help others starting their own deep learning journey .

In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation. We prove that sparse filtering can perform adaptation only if the conditional distribution of the labels has a structure explained by a cosine metric. To overcome this limitation, we propose a new algorithm, named periodic sparse filtering, and carry out the same theoretical analysis regarding covariate shift adaptation. We show that periodic sparse filtering can perform adaptation under the looser and more realistic requirement that the conditional distribution of the labels has a periodic structure, which may be satisfied, for instance, by user-dependent data sets. We experimentally validate our theoretical results on synthetic data. Moreover, we apply periodic sparse filtering to real-world data sets to demonstrate that this simple and computationally efficient algorithm is able to achieve competitive performances.

We consider the problem of recovering a $d-$dimensional manifold $\mathcal{M} \subset \mathbb{R}^n$ when provided with noiseless samples from $\mathcal{M}$. There are many algorithms (e.g., Isomap) that are used in practice to fit manifolds and thus reduce the dimensionality of a given data set. Ideally, the estimate $\mathcal{M}_\mathrm{put}$ of $\mathcal{M}$ should be an actual manifold of a certain smoothness; furthermore, $\mathcal{M}_\mathrm{put}$ should be arbitrarily close to $\mathcal{M}$ in Hausdorff distance given a large enough sample. Generally speaking, existing manifold learning algorithms do not meet these criteria. Fefferman, Mitter, and Narayanan (2016) have developed an algorithm whose output is provably a manifold. The key idea is to define an approximate squared-distance function (asdf) to $\mathcal{M}$. Then, $\mathcal{M}_\mathrm{put}$ is given by the set of points where the gradient of the asdf is orthogonal to the subspace spanned by the largest $n - d$ eigenvectors of the Hessian of the asdf. As long as the asdf meets certain regularity conditions, $\mathcal{M}_\mathrm{put}$ is a manifold that is arbitrarily close in Hausdorff distance to $\mathcal{M}$. In this paper, we define two asdfs that can be calculated from the data and show that they meet the required regularity conditions. The first asdf is based on kernel density estimation, and the second is based on estimation of tangent spaces using local principal components analysis.

Natural Language Processing, or NLP, is a subfield of machine learning concerned with understanding speech and text data. Statistical methods and statistical machine learning dominate the field and more recently deep learning methods have proven very effective in challenging NLP problems like speech recognition and text translation. In this post, you will discover the Stanford course on the topic of Natural Language Processing with Deep Learning methods. This course is free and I encourage you to make use of this excellent resource. The course is taught by Chris Manning and Richard Socher.

Are you interested in data science and machine learning? If yes, consider looking at the courses we list in this post. These online classes will help you get a head start in this field. You can start and stop at any time, no hard and fast rules at all. The best part is, these courses are often available for just $10 to $15. Content: The course consists of 277 lectures, a total of 40.5 hours of video lessons.