Results


How to Train your Self-Driving Car to Steer – Towards Data Science – Medium

#artificialintelligence

Neural networks, and particularly deep learning research, have obtained many breakthroughs recently in the field of computer vision and other important fields in computer science. Deep neural networks, especially in the field of computer vision, object recognition and so on, have often a lot of parameters, millions of them. It's a quite recent model that achieved remarkable performances on object recognition tasks with very few parameters, and weighting just some megabytes. I added a recurrent layer to the output of one of the first densely connected layers of SqueezeNet: the network now takes as input 5 consecutive frames, and then the recurrent layers outputs a single real-valued number, the steering angle.


Automated Machine Learning: Deploying AutoML to the Cloud

#artificialintelligence

In this post, I share an AutoML setup to train and deploy pipelines in the cloud using Python, Flask, and two AutoML frameworks that automate feature engineering and model building. I tested and combined two open source Python tools: tsfresh, an automated feature engineering tool, and, TPOT, an automated feature preprocessing and model optimization tool. After an optimal feature engineering and model building pipeline is determined, our pipeline is persisted within our Flask application within a Python dictionary–the dictionary key being the pipeline id specified in the parameter file. I have shown how to make use of open source AutoML tools and operationalize a scalable automated feature engineering and model building pipeline to the cloud.


Alison machine learning predicts mobile ad campaign results

#artificialintelligence

YellowHead has launched Alison, a machine learning technology that predicts how mobile advertising campaigns, known as paid user acquisition, will turn out. It specializes in paid user acquisition campaigns, app store optimization, and search engine optimization. And now it has added Alison to use machine learning to predict a campaign's performance in the hopes of uncovering more insights for brands and wasting less advertising money. Top university math professors at the Data Science Research Team at Tel Aviv University and the company's developers worked on Alison, which supplements human intelligence to optimize campaigns based on predicted results across multiple ad platforms such as Facebook and Google.


Pseudo-labeling a simple semi-supervised learning method - Data, what now?

@machinelearnbot

In this post, I will show how a simple semi-supervised learning method called pseudo-labeling that can increase the performance of your favorite machine learning models by utilizing unlabeled data. First, train the model on labeled data, then use the trained model to predict labels on the unlabeled data, thus creating pseudo-labels. In competitions, such as ones found on Kaggle, the competitor receives the training set (labeled data) and test set (unlabeled data). Pseudo-labeling allows us to utilize unlabeled data while training machine learning models.


How to Set Up Distributed XGBoost on MapR-FS

#artificialintelligence

In this blog post, I'll provide step-by-step instructions for setting up XGBoost under a 3-node Hadoop cluster (Ubuntu EC2 instances). We achieved good performance by running XGBoost through a Message Passing Interface (MPI) and MapR-FS, and we recommend setting up POSIX clients for XGBoost training tasks. In addition to running XGBoost on MapR cluster nodes, I recommend that you run XGBoost on MapR POSIX clients. Also, running XGBoost on MPI won't affect the YARN resource management on MapR cluster nodes very much.


DART: Dropout Regularization in Boosting Ensembles

#artificialintelligence

The idea of DART is to build an ensemble by randomly dropping boosting tree members. The percentage of dropouts can determine the degree of regularization for boosting tree ensembles. For the comparison purpose, we first developed a boosting tree ensemble without dropouts, as shown below. As shown below, by dropping 10% tree members, ROC for the testing set can increase from 0.60 to 0.65.


Churn Prediction With Apache Spark Machine Learning - DZone AI

#artificialintelligence

Let's go through an example of telecom customer churn: Decision trees create a model that predicts the class or label based on several input features. Spark ML supports k-fold cross validation with a transformation/estimation pipeline to try out different combinations of parameters, using a process called grid search, where you set up the parameters to test, and a cross validation evaluator to construct a model selection workflow. It's not surprising that these feature numbers map to the fields Customer service calls and Total day minutes. In this blog post, we showed you how to get started using Apache Spark's machine learning decision trees and ML pipelines for classification.


Machine Learning: An In-Depth Guide – Model Performance and Error Analysis

#artificialintelligence

These include: true positives, false positives (type 1 error), true negatives, and false negatives (type 2 error). There are many metrics for determining model performance for regression problems, but the most commonly used metric is known as the mean square error (MSE), or variation called the root mean square error (RMSE), which is calculated by taking the square root of the mean squared error. Recall the different results from a binary classifier, which are true positives, true negatives, false positives, and false negatives. Precision (positive predictive value) is the ratio of true positives to the total amount of positive predictions made (i.e., true or false).


machine-learning-an-in-depth-non-technical-guide-part-4

#artificialintelligence

These include: true positives, false positives (type 1 error), true negatives, and false negatives (type 2 error). There are many metrics for determining model performance for regression problems, but the most commonly used metric is known as the mean square error (MSE), or variation called the root mean square error (RMSE), which is calculated by taking the square root of the mean squared error. Recall the different results from a binary classifier, which are true positives, true negatives, false positives, and false negatives. Precision (positive predictive value) is the ratio of true positives to the total amount of positive predictions made (i.e., true or false).


Regularization in Logistic Regression: Better Fit and Better Generalization?

@machinelearnbot

Regularization does NOT improve the performance on the data set that the algorithm used to learn the model parameters (feature weights). However, it can improve the generalization performance, i.e., the performance on new, unseen data, which is exactly what we want. Now, if we regularize the cost function (e.g., via L2 regularization), we add an additional to our cost function (J) that increases as the value of your parameter weights (w) increase; keep in mind that the regularization we add a new hyperparameter, lambda, to control the regularization strength. Therefore, our new problem is to minimize the cost function given this added constraint.