GANs are a type of deep neural network that allow us to generate data. In this webinar, we'll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We'll explore the GAN framework along with its components -- generator and discriminator networks. We'll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we'll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.

This five-course accelerated specialization is designed for data professionals who are responsible for designing, building, analyzing, and optimizing big data solutions. Through a combination of video lectures, quizzes, and hands-on labs, you'll learn how to carry out serverless data analysis and productionize machine learning models. This specialization is designed to give participants a robust hands-on experience and is primarily lab-focused. Learn how to deliver business value with Big Data and Machine Learning Solutions on Google Cloud Platform. To get up to speed quickly, follow the courses in this specialization.

This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models and TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. This course is your guide to exploring the possibilities with deep learning; it will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With this video course, you will dig your teeth deeper into the hidden layers of abstraction using raw data.

SparkR is an R package that provides a light-weight frontend to use Apache Spark from R. In Spark 2.0.2, SparkR provides a distributed data frame implementation that supports operations like selection, filtering, aggregation etc. (similar to R data frames) but on large datasets. SparkR also supports distributed machine learning using MLlib. You will learn how to create spark cluster in Databricks. You will learn how to create dataframes and grouping data and aggregating data.

In this day and age of digital marketing, having the right visual content can increase the brand value significantly. But not everyone is equipped to create professional graphics and videos on their own. This means people spend thousands of dollars in hiring designers or buying expensive softwares, which have deep learning curves and are not easy to use. The solution would be to use readily-available tools like PowerPoint or use free online tools which are easy to use. With Visual Deck, you can learn to create your own professional graphics, presentations and animated videos with no prior experience whatsoever!

You will engage in 5 hands-on labs for creating advanced models …. Because our goal is to get you up to speed as quickly as possible, we'll cover through the 30 lectures, The course will enable you to extract actionable insight from your customers' data According to a Mckinsey Study, demand for data scientists is projected to exceed supply by more than 50% by 2018. That's the gap you as citizen Data Scientists are going to fill Citizen data scientist are "Business people with the right attitude - curious, adventurous, determined - to research and improve things in the organization" SAS The need is so, that according to Gartner, by 2017, the number of citizen data scientists will grow 5 times faster than the number of highly skilled data scientists. This course is designed for business professionals: marketer, manager and analytical minds in every department … who want to take their skills to the next level. If you have solid business knowledge, curious, determined to improve things in your company or just willing to learn new methods and tools than this course is for you….

Are you a data analyst, data scientist, or a researcher looking for a guide that will help you increase the speed and efficiency of your machine learning activities? If yes, then this course is for you! Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. It has helped engineers, researchers, and many others make significant progress with everything from voice/sound recognition to language translation and face recognition. It has also proved to be useful in the early detection of skin cancer and preventing blindness in diabetics.

We use different equations depending on the number of output classes. With 2 classes, we will use binomial cross entropy for loss and more than 2 classes involves using the cross-entropy with softmax. Example of how to calculate the cross-entropy loss for a 3 class problem. With the multinomial cross entropy, you can see that we only keep the loss contribution from the correct class. Usually, with neural nets, this will be case if our ouputs are sparse (just 1 true class).

Principal component analysis (PCA) is very popular to perform dimension reduction. The selection of the number of significant components is essential but often based on some practical heuristics depending on the application. Only few works have proposed a probabilistic approach able to infer the number of significant components. To this purpose, this paper introduces a Bayesian nonparametric principal component analysis (BNP-PCA). The proposed model projects observations onto a random orthogonal basis which is assigned a prior distribution defined on the Stiefel manifold. The prior on factor scores involves an Indian buffet process to model the uncertainty related to the number of components. The parameters of interest as well as the nuisance parameters are finally inferred within a fully Bayesian framework via Monte Carlo sampling. A study of the (in-)consistence of the marginal maximum a posteriori estimator of the latent dimension is carried out. A new estimator of the subspace dimension is proposed. Moreover, for sake of statistical significance, a Kolmogorov-Smirnov test based on the posterior distribution of the principal components is used to refine this estimate. The behaviour of the algorithm is first studied on various synthetic examples. Finally, the proposed BNP dimension reduction approach is shown to be easily yet efficiently coupled with clustering or latent factor models within a unique framework.