Initialize H2O Cluster With All Available Threads One should use h2o.shutdown() if changing parameters below. Also, setting assertion FALSE seems to help with stability of H20.

An Outlier is an observation or point that is distant from other observations/points. But, how would you quantify the distance of an observation from other observations to qualify it as an outlier. Outliers are also referred to as observations whose probability to occur is low. But, again, what constitutes low?? There are parametric methods and non-parametric methods that are employed to identify outliers.

I am a seasoned Analytics professional with 15 years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios.

Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. Completely Practical: This course has just enough theory to get you started with both Quant Trading and Machine Learning.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. Read data files and perform regression machine learning operations by installing related packages and running script code on RStudio IDE. Approximate ensemble methods such as random forest regression and gradient boosting machine regression to enhance decision tree regression prediction accuracy. Analyze multi-layer perceptron methods such as optimal number of hidden nodes artificial neural network. Read data files and perform regression machine learning operations by installing related packages and running script code on RStudio IDE.

Community detection has become very popular in network analysis the last decades. Its range of applications include social sciences, biology and complex systems, such as the worldwide-web, protein-protein interactions, or social networks (see [5] for a thorough exposition of the topic). To tackle this problem, spectral approaches have been introduced in [12] or [18], inspired from the so-called spectral clustering problem (see [10]). However, the treatment of larger and larger graphs has been investigated and modularity-based algorithms has been proposed. This class of algorithms maximize a quality index called modularity, introduced in [13].

In this paper, we consider the problem of event classification with multi-variate time series data consisting of heterogeneous (continuous and categorical) variables. The complex temporal dependencies between the variables combined with sparsity of the data makes the event classification problem particularly challenging. Most state-of-art approaches address this either by designing hand-engineered features or breaking up the problem over homogeneous variates. In this work, we propose and compare three representation learning algorithms over symbolized sequences which enables classification of heterogeneous time-series data using a deep architecture. The proposed representations are trained jointly along with the rest of the network architecture in an end-to-end fashion that makes the learned features discriminative for the given task. Experiments on three real-world datasets demonstrate the effectiveness of the proposed approaches.

Accurately predicting drug responses to cancer is an important problem hindering oncologists' efforts to find the most effective drugs to treat cancer, which is a core goal in precision medicine. The scientific community has focused on improving this prediction based on genomic, epigenomic, and proteomic datasets measured in human cancer cell lines. Real-world cancer cell lines contain noise, which degrades the performance of machine learning algorithms. This problem is rarely addressed in the existing approaches. In this paper, we present a noise-filtering approach that integrates techniques from numerical linear algebra and information retrieval targeted at filtering out noisy cancer cell lines. By filtering out noisy cancer cell lines, we can train machine learning algorithms on better quality cancer cell lines. We evaluate the performance of our approach and compare it with an existing approach using the Area Under the ROC Curve (AUC) on clinical trial data. The experimental results show that our proposed approach is stable and also yields the highest AUC at a statistically significant level.

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network. The alternating back-propagation algorithm iterates the following two steps: (1) Inferential back-propagation, which infers the latent factors by Langevin dynamics or gradient descent. (2) Learning back-propagation, which updates the parameters given the inferred latent factors by gradient descent. The gradient computations in both steps are powered by back-propagation, and they share most of their code in common. We show that the alternating back-propagation algorithm can learn realistic generator models of natural images, video sequences, and sounds. Moreover, it can also be used to learn from incomplete or indirect training data.

This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of synchronization protocol, stale gradient updates, minibatch size, learning rates, and number of learners on runtime performance and model accuracy. We introduce a new learning rate modulation strategy to counter the effect of stale gradients and propose a new synchronization protocol that can effectively bound the staleness in gradients, improve runtime performance and achieve good model accuracy. Our empirical investigation reveals a principled approach for distributed training of neural networks: the mini-batch size per learner should be reduced as more learners are added to the system to preserve the model accuracy. We validate this approach using commonly-used image classification benchmarks: CIFAR10 and ImageNet.