In this article, a semi-supervised classification algorithm implementation will be described using Markov Chains and Random Walks. We have the following 2D circles dataset (with 1000 points) with only 2 points labeled (as shown in the figure, colored red and blue respectively, for all others the labels are unknown, indicated by the color black). Now the task is to predict the labels of the other (unlabeled) points. From each of the unlabeled points (Markov states) a random walk with Markov transition matrix (computed from the row-stochastic kernelized distance matrix) will be started that will end in one labeled state, which will be an absorbing state in the Markov Chain.
The below is an example of how sklearn in Python can be used to develop a k-means clustering algorithm. The purpose of k-means clustering is to be able to partition observations in a dataset into a specific number of clusters in order to aid in analysis of the data. From this perspective, it has particular value from a data visualisation perspective.
In this post we will see how to include the effect of predictors in non-linear regressions. In other words, letting the parameters of non-linear regressions vary according to some explanatory variables (or predictors). Be sure to check the first post on this if you are new to non-linear regressions. The example that I will use throughout this post is the logistic growth function, it is often used in ecology to model population growth. For instance, say you count the number of bacteria cells in a petri dish, in the beginning the cell counts will increase exponentially but after some time due to limits in resources (be it space or food), the bacteria population will reach an equilibrium. This will produce the classical S-shaped, non-linear, logistic growth function. The logistic growth function has three parameters: the growth rate called “r”, the population size at equilibrium called “K” and the population size at the beginning called “n0”.
Hey, remember when I wrote those ungodly long posts about matrix factorization chock-full of gory math? Good news! You can forget it all. We have now entered the Era of Deep Learning, and automatic differentiation shall be our guiding light. Less facetiously, I have finally spent some time checking out these new-fangled deep learning frameworks, and damn if I am not excited. In this post, I will show you how to use PyTorch to bypass the mess of code from my old post on Explicit Matrix Factorization and instead implement a model that will converge faster in fewer lines of code. But first, let’s take a trip down memory lane.
At Indeed, machine learning is key to our mission of helping people get jobs. Machine learning lets us collect, sort, and analyze millions of job postings a day. In this post, we’ll describe our open-source Java wrapper for a particularly useful machine learning library, and we’ll explain how you can benefit from our work.
Challenges of machine learning
It’s not easy to build a machine learning system. A good system needs to do several things right:
• Feature engineering. For example, converting a text to features vector requires you to precalculate statistics about words. This process can be challenging.
• Model quality. Most algorithms require hyper parameters tuning, which is usually done through grid search. This process can take hours, making it hard to iterate quickly on ideas.
• Model training for large datasets. The implementations for most algorithms assume that the entire dataset fits in memory in a single process. Extremely large datasets, like those we work with at Indeed, are harder to train.
Max-pooling is a procedure in a neural network which has several benefits. It performs dimensionality reduction by taking a collection of neurons and reducing them to a single value for future layers to receive as input. It can also prevent overfitting, since it takes a large set of inputs and admits only one value, making it harder to memorize the input. In this episode, we discuss the intuitive interpretation of max-pooling and why it’s more common than mean-pooling or (theoretically) quartile-pooling.
I discuss the concept of a “neural network” by providing some examples of recent successes in neural network machine learning algorithms and providing a historical perspective on the evolution of the neural network concept from its biological origins.
Over the last decade, the application and performance of Deep Learning has progressed at an astonishing rate. However, the current state of the field is that the neural network architectures are highly specialized to specific domains of application. An important question remains unanswered: Will a convergence between these domains facilitate a unified model capable of performing well across multiple domains? Today, we present MultiModel, a neural network architecture that draws from the success of vision, language and audio networks to simultaneously solve a number of problems spanning multiple domains, including image recognition, translation and speech recognition. While strides have been made in this direction before, namely in Google’s Multilingual Neural Machine Translation System used in Google Translate, MultiModel is a first step towards the convergence of vision, audio and language understanding into a single network. The inspiration for how MultiModel handles multiple domains comes from how the brain transforms sensory input from different modalities (such as sound, vision or taste), into a single shared representation and back out in the form of language or actions. As an analog to these modalities and the transformations they perform, MultiModel has a number of small modality-specific sub-networks for audio, images, or text, and a shared model consisting of an encoder, input/output mixer and decoder, as illustrated below.
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and compare the results.
Today, we are happy to release Tensor2Tensor (T2T), an open-source system for training deep learning models in TensorFlow. T2T facilitates the creation of state-of-the art models for a wide variety of ML applications, such as translation, parsing, image captioning and more, enabling the exploration of various ideas much faster than previously possible. This release also includes a library of datasets and models, including the best models from a few recent papers (Attention Is All You Need, Depthwise Separable Convolutions for Neural Machine Translation and One Model to Learn Them All) to help kick-start your own DL research.
What is Jupyter, and why do you care? After all, Jupyter has never become a buzzword like data science, artificial intelligence, or Web 2.0. Unlike those big abstractions, Jupyter is very concrete. It’s an open source project, a piece of software, that does specific things.
But without attracting the hype, Jupyter Notebooks are revolutionizing the way engineers and data scientists work together. If all important work is collaborative, the most important tools we have are tools for collaboration, tools that make working together more productive.
That’s what Jupyter is, in a nutshell: it’s a tool for collaborating. It’s built for writing and sharing code and text, within the context of a web page. The code runs on a server, and the results are turned into HTML and incorporated into the page you’re writing. That server can be anywhere: on your laptop, behind your firewall, or on the public internet. Your page contains your thoughts, your code, and the results of running the code.
Some weeks ago, I was working on a dataviz to show results coming from an analysis I had performed, and I found myself looking at that default ggplot2 palette, which is optimal in term of discrimination among categories, but nevertheless can not be compared to some wonderful palettes you can see employed within art masterpieces like Monet impression,soleil levant or Michelangelo’s Tondo Doni. Those palettes are coming from years and years of studies around colour theory and pictorial techniques and, I started thinking, would do a great service if employed as plot’s palettes, with their complementary colours or their balanced set of hues.
This is the third part of our data visualization series and at this part we will explore the features of two more of the charts that googleVis provides. Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin!
The sparklyr package (by RStudio) provides a high-level interface between R and Apache Spark. Among many other things, it allows you to filter and aggregate data in Spark using the dplyr syntax. In Microsoft R Server 9.1, you can now connect to a a Spark session using the sparklyr package as the interface, allowing you to combine the data-preparation capabilities of sparklyr and the data-analysis capabilities of Microsoft R Server in the same environment. In a presentation by at the Spark Summit (embedded below, and you can find the slides here), Ali Zaidi shows how to connect to a Spark session from Microsoft R Server, and use the sparklyr package to extract a data set. He then shows how to build predictive models on this data (specifically, a deep Neural Network and a Boosted Trees classifier). He also shows how to build general ensemble models, cross-validate hyper-parameters in parallel, and even gives a preview of forthcoming streaming analysis capabilities.
Bias vs Variance tradeoff is always encountered in applying supervised learning algorithms. Least squares regression provides a good fit for the training set but can suffer from high variance which lowers predictive ability. To counter this problem, we can regularize the beta coefficients by employing a penalization term. Ridge regression applies l2 penalty to the residual sum of squares. In contrast, LASSO regression, which was covered here previously, applies l1 penalty. Using ridge regression, we can shrink the beta coefficients towards zero which would reduce variance at the cost of higher bias which can result in better predictive ability than least squares regression. In this exercise set we will use the glmnet package (package description: here) to implement ridge regression in R.