Microsoft Custom Vision: Retrain Model in C#

In the previous post, we went over how to use the Custom Vision Training and Prediction SDKs to programmatically predict image URLs and image files. In this post, we’re going to use those same SDKs to show how to programmatically upload more training images to the service and train a new model with those new…

Microsoft Custom Vision: Creating an Image Classification Model

Creating a model to classify images would usually involve creating your own deep learning model from scratch. This includes having a very large and diverse set of training images with a portion of them set aside as a test set, a good convolutional neural network as the model, and a GPU enabled machine to do…

Microsoft Custom Vision: Predict Images with C#

In the previous post, we showed how to train an image classification model using the Microsoft Custom Vision service as well as to perform a quick test on a new image. However, what if you want to integrate this model into one of your applications that is using C#? Whether it’s an app that runs…

Evaluate Your Models with Cross Validation in ML.NET

Let’s say you’ve been working on a machine learning model and your initial evaluation on test data looks good but is that the same kind of performance you’ll get once you deploy your model to take on actual data it hasn’t seen before? This can happen if your model has overfitted to your data. We…

Save and Read Models in ML.NET

Often times you’ll be iterating on your model to try to get it to perform well with new data, so you’ll be training on it for each of those iterations. However, once you feel like you have a model that you believe is good to use, what do you do next? In this post, we’ll…

Machine Learning with C#: An Introduction to ML.NET

When you think of data science and machine learning two programming languages are going to instantly pop into your mind: Python and R. These two languages are great and I love working with them, but coming from a .NET and C# background myself it would be nice to see some love for data science in…

Wintellect Accepted Into the Microsoft AI Inner Circle Program

Wintellect is proud to have been selected as one of an elite group of invitation-only Microsoft partners to join Microsoft’s new AI Inner Circle Partner Program. “We are excited to be part of this exclusive group, and look forward to deepening our partnership with Microsoft and jointly advancing business transformations for our customers with AI,”…

Using the Cognitive Services Text Analytics API: Sentiment Analysis

In our previous post, we feed Twitter data to the Text Analytics API which was able to detect the language of each tweet. We will expand upon our previous work and continue to use the API and our Twitter data to determine the sentiment of each tweet. By analyzing the sentiment of each tweet, we’re…

Building Language Intelligent Apps with Microsoft’s LUIS

To aid in building applications that have better natural language understanding, Microsoft came out with LUIS or Language Understanding Intelligent Services. LUIS can be used for understanding speech for the Bot Framework, Bing Speech, or even with Cortana. In this post, we’ll learn how to create a LUIS app, apply basic LUIS concepts, and how…

Pre-processing Text Data with NLTK and Azure Machine Learning

Data comes in all forms. Lately, we’ve been going over mostly numerical and categorical data. Even though the categorical data contains words, we transform it into numerical data for our algorithms. However, what if your data is only words? That’s where natural language processing comes in, and in this post, we’ll go over the basics…