One magical aspect of software is that it just keeps working. If you code a calculator app, it will still correctly add and multiply numbers a month, a year, or 10 years later. The fact that the marginal cost of software approaches zero has been a bedrock of the software industry's business model since the 1980s. This is no longer the case when you are deploying machine learning (ML) models. Making this faulty assumption is the most common mistake of companies taking their first artificial intelligence (AI) products to market.
We're excited to announce the preview of Automated Machine Learning (AutoML) for Dataflows in Power BI. AutoML enables business analysts to build machine learning models with clicks, not code, using just their Power BI skills. Power BI Dataflows offer a simple and powerful ETL tool that enables analysts to prepare data for further analytics. You invest significant effort in data cleansing and preparation, creating datasets that can be used across your organization. AutoML enables you to leverage your data prep effort for building machine learning models directly in Power BI. With AutoML, the data science behind the creation of ML models is automated by Power BI, with guardrails to ensure model quality, and visibility to ensure you have full insight into the steps used to create your ML model.
You just learn how to build and train 5 deep learning models for classification problems using Tensorflow. One more thing about adding pooling layer is that because of the pooling, the image size is gradually shrinking. Early convolutional weights often train to detect simple edges, while successive convolutional layers combine those edges into gradually more complex shapes such as faces, cars, and even dogs. Human learning is the beginning of Deep learning!
Even though pushing your Machine Learning model to production is one of the most important steps of building a Machine Learning application there aren't many tutorials out there showing how to do so. Therefore in this article, I will go over how to productionize a Ludwig model by building a Rest-API as well as a normal website using the Flask web micro-framework. The same process can be applied to almost any other machine learning framework with only a few little changes. Both the website and RESTful-API we will build in this article are very simple and won't scale very well. Therefore in the next article, we will take a look at how to scale our Flask website to handle multiple recurrent requests, which is also shown in this excellent article.
A data set is called imbalanced if it contains many more samples from one class than from the rest of the classes. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the majority class but very poor accuracy on the minority class(es) due to the influence that the larger majority class. The common example of such dataset is credit card fraud detection, where data points for fraud 1, are usually very less in comparison to fraud 0. There are many reasons why a dataset might be imbalanced: the category one is targeting might be very rare in the population, or the data might simply be difficult to collect. Let's solve the problem of an imbalanced dataset by working on one such dataset.
Evaluating machine learning models for bias is becoming an increasingly common focus for different industries and data researchers. Model Fairness is a relatively new subfield in Machine Learning. In the past, the study of discrimination emerged from analyzing human-driven decisions and the rationale behind those decisions. Since we started to rely on predictive ML models to make decisions for different industries such as insurance and banking, we need to implement strategies to ensure the fairness of those models and detect any discriminative behaviour during predictions. As ML models get more complex, it becomes much harder to interpret them.
Have you ever wondered how combining weak predictors can yield a strong predictor? Ensemble Learning is the answer! This is the first of a pair of articles in which I will explore ensemble learning and bootstrapping, both the theoretical basics and real-life use cases. Bootstrapping is a resampling method where observations are drawn from a sample with replacement. Let's say you have 1,000 data points, and you create 100 distinct samples of 1,000 data points each by drawing from the original sample only, with replacement.
In this paper, we aim at providing an introduction to the gradient descent based optimization algorithms for learning deep neural network models. Deep learning models involving multiple nonlinear projection layers are very challenging to train. Nowadays, most of the deep learning model training still relies on the back propagation algorithm actually. In back propagation, the model variables will be updated iteratively until convergence with gradient descent based optimization algorithms. Besides the conventional vanilla gradient descent algorithm, many gradient descent variants have also been proposed in recent years to improve the learning performance, including Momentum, Adagrad, Adam, Gadam, etc., which will all be introduced in this paper respectively.
As part of my own learning, continuing from Part 1 and trying to improve our neural network model, we will use some of the well-known machine learning techniques mentioned on TensorFlow. In the previous article, we saw certain problems with our training. Here, we will address them and see if our results improve as we go. A model is considered to overfit when it performs with great accuracy on the training data (data used for training the model), but when evaluated against a test or unseen data set, it performs rather poorly. This happens because our model has overfit the data.
ProPublica's analysis of the COMPAS tool found that black defendants were far more likely than white defendants to be incorrectly judged to be at a higher risk of recidivism, while white defendants were more likely than black defendants to be incorrectly flagged at low risk. Northpointe, the company behind the tool responded by saying that the model was not unfair because it had several similar overall performances for both white people and black people. The table above presents the results from each model for the outcomes of any arrest for African American and White men. The AUCs for African American men range from .64 to .73 while for the whites, it ranges from .69 to .75. Northpoint hence concluded that since the AUC results for White men are quite similar to the results for African American men, their algorithm is completely fair.