We compare two statistical models of three binary random variables. One is a mixture model and the other is a product of mixtures model called a restricted Boltzmann machine. Although the two models we study look different from their parametrizations, we show that they represent the same set of distributions on the interior of the probability simplex, and are equal up to closure. We give a semi-algebraic description of the model in terms of six binomial inequalities and obtain closed form expressions for the maximum likelihood estimates. We briefly discuss extensions to larger models.

Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional networks and other graph neural networks all focus on learning node-level representation rather than graph-level representation. Previous works simply sum all feature vectors for all nodes in the graph to obtain the graph feature vector for drug predication. In this paper, we introduce a dummy super node that is connected with all nodes in the graph by a directed edge as the representation of the graph and modify the graph operation to help the dummy super node learn graph-level feature. Thus, we can handle graph-level classification and regression in the same way as node-level classification and regression. In addition, we apply focal loss to address class imbalance in drug datasets. The experiments on MoleculeNet show that our method can effectively improve the performance of molecular properties predication.

We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits. The dataset is freely available at https://github.com/zalandoresearch/fashion-mnist

We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides useful cues for reasoning about joint locations. The mapping from raw depth to segmentation maps is learned in a semi/weakly-supervised way from two different datasets: (i) a synthetic dataset created through a rendering pipeline including densely labeled ground truth (pixelwise segmentations); and (ii) a dataset with real images for which ground truth joint positions are available, but not dense segmentations. Loss for training on real images is generated from a patch-wise restoration process, which aligns tentative segmentation maps with a large dictionary of synthetic poses. The underlying premise is that the domain shift between synthetic and real data is smaller in the intermediate representation, where labels carry geometric and topological meaning, than in the raw input domain. Experiments on the NYU dataset show that the proposed training method decreases error on joints over direct regression of joints from depth data by 15.7%.

Artificial intelligence (AI) is a lot like life's relationships. Sometimes what you put into it is pretty straightforward, leading to the output or outcome that you wanted. Other times, let's just say, the process gets a bit more convoluted and sometimes the outcome isn't exactly what you envisioned. In other words, you may input the same into both relationships, but different paths lead you to different results. Nevertheless, both are learning processes.

When delving into the world of machine learning (ML), choosing one framework from many alternatives can be an intimidating task. You might already be familiar with the names, but it's useful to evaluate the options during the decision-making process. There are different frameworks, libraries, applications, toolkits, and datasets in the machine learning world that can be very confusing, especially if you're a beginner. Being accustomed to the popular ML frameworks is necessary when it comes to choosing one to build your application. This is why we compiled a list of the top 10 machine learning frameworks.

But automotive OEMs need to take five steps to overcome challenges and position themselves to succeed. Artificial intelligence (AI) is the word on everyone's lips. But in the automotive industry today, many products and services being labeled as such are in fact reliant on a form of advanced analytics (evolving from conventional algorithms) that enables those features--for example, predictive maintenance in manufacturing. Theories of AI have existed since 1950. However, AI itself gained wider functional applicability only in the past few decades, with the rise of machine learning and deep learning.

It's safe to say that Python is a pretty popular tool across a whole range of industries and professions, thanks, no doubt, to the programming language's accessibility, wealth of libraries and frameworks, and of course, its huge community of die-hard devs that claim Python should be the tool of choice for any self-respecting developer. Packt's 2017 Skill Up survey, backed up these claims when it revealed that Python is the most-used tool for tech professionals across a range of vastly different job roles, slithering its way up from the number 2 spot in 2016. We asked Sebastian Raschka, applied machine learning and deep learning researcher and the author of Packt's best-selling book Python Machine Learning, why he always turns to Python and what's next for what is perhaps undeniably the most popular language of the last two decades. Here's what he had to say.

This whole technology of putting artificial brain into machine is really fascinating, we humans because of our ingenious intellect has this natural instincts to go beyond what seems impossible to create tools and technology which becomes an extension to our day to day life, which can make decisions on our part and make our living super efficient. It is with this urge to make humans super productive we started putting artificial intelligence to the computer machines and now we have come a long way to build machines which can nearly think like human brains. Today we will see how Deep learning a branch of ML is really doing justice to all those valuable data floating around in this universe and processing it efficiently to help us reach to some rational conclusions in the filed of Speech recognition, Image Recognition, NLP, Healthcare, Financial Sector etc.. As we know that various Machine Learning techniques has been used to process our raw data to help us is content filtering on social network, to write recommendation engine for e-commerce based portals, In Image and Pattern recognitions, to transcribe speech to text etc. In 1986 Rina Dechter coined the expression Deep Learning for the first time, but Ivakhnenko & Lapa in 1965 wrote the first working learning algorithm for supervised, deep, feedforward, multilayer perceptrons was published by Ivakhnenko and Lapa in 1965.These ideas were implemented in a computer identification system by the World School Council London called "Alpha", which demonstrated the learning process.

Every day we use different technologies without even knowing how exactly they work. In fact, it's not very easy to understand engines powered by machine learning. The Statsbot team wants to make machine learning clear by telling data stories in this blog. Today, we've decided to explore machine translators and explain how the Google Translate algorithm works. Years ago, it was very time consuming to translate the text from an unknown language.