There are many popular machine learning libraries for Python. There’s TensorFlow, scikit-learn, Theano, Caffe, and many others.

And in the NET domain we have Microsoft’s new ML.NET machine learning library which can be used in C# and F# applications.

But now Microsoft has created NimbusML, a new library that will let you access the ML.NET machine learning library directly in your Python code!

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NimbusML acts as a bridge between the Python process that’s running your app code and the dotNET runtime that’s hosting the ML.NET library. All calls are transparently routed between Python and dotNET.

Naturally I had to try it out. I decided to port my New York taxi price prediction model to NimbusML to see what happens. …


In this article, I’m going to build an app that recognizes handwritten digits from the famous MNIST machine learning dataset:

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The MNIST challenge requires machine learning models to read images of handwritten digits and correctly predict which digit is visible in each image.

This may seem like an easy challenge, but look at this:

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These are actual digits from the dataset. Are you able to identify each one? It probably won’t surprise you to hear that the human error rate on this exercise is about 2.5%.

There are 70,000 images of 28x28 pixels in the dataset. I’m going to use a truncated set of 5,000 images to speed up the training. …


In this article, I am going to build an F# app with ML.NET and NET Core that reads medical data and predicts if a patient has a risk of heart disease. I will show you how to do this with only 120 lines of code.

ML.NET is Microsoft’s new machine learning library. It can run linear regression, logistic classification, clustering, deep learning, and many other machine learning algorithms.

NET Core is the Microsoft multi-platform NET Framework that runs on Windows, OS/X, and Linux. It’s the future of cross-platform NET development.

And F# is a perfect language for machine learning. It’s a 100% pure functional programming language based on OCaml and inspired by Python, Haskell, Scala, and Erlang. It has a powerful syntax and lots of built-in classes and functions for processing data. …


Building machine learning apps has never been easier!

Because we have ML.NET, Microsoft’s new machine learning library. It can run linear regression, logistic classification, clustering, deep learning, and many other machine learning algorithms.

But did you know that the F# language is the perfect choice for developing machine learning applications with ML.NET?

The F# language is just perfect for machine learning. It’s a 100% pure functional programming language based on OCaml and inspired by Python, Haskell, Scala, and Erlang. It has a powerful syntax and lots of built-in classes and functions for processing data.

Check out the following F# code fragment that trains a machine learning model to predict taxi fares in New York city, and then uses the fully-trained model to predict a single trip for a passenger paying with a credit card. …


In this article I’m going to build a specialized neural network architecture called a Generative Adversarial Network (GAN).

GANs are weird. Here’s what they look like:

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The Generator is a convolutional neural network (CNN) laid out in reverse.

A normal CNN reads in an image and outputs a list of class probabilities which usually indicate if the image contains a person, animal, or object.

But a reverse CNN does the opposite: we create a 1-dimensional class vector (just a list of numbers) and the network will convert this information to a fully realized machine-generated color image. …


Style transfer is a process where we recompose an image in the style of another image by transferring the artistic style from one picture to another using a convolutional neural network.

It looks like this:

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This is a photo of Pablo Picasso painting a bull on a sheet of glass, but the image has been repainted by a neural network using the artistic style of another painting.

You can watch the full video here: https://www.youtube.com/watch?v=FzvTLEB_3KY

Pretty cool, right?

This is a short fragment from Visite à Picasso, a 1950 film by Belgian filmmaker Paul Haesaerts, in which Picasso demonstrates his skills. …


In this article I’m going to build an app that can automatically detect the sentiment of an English text.

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I actually tried this already in this article where I used a 1-dimensional convolutional neural network to analyze the movie review text. That approach worked quite well with a final accuracy of 86%, but unfortunately my solution started overfitting right away.

A much better way to analyze English text is by using a specialized type of recurrent neural network called an LSTM network.

All recurrent neural networks have an internal state (a type of memory) that helps them make sense of written language. But an LSTM network actually has two types of memory: long-term and short-term memory. …


In this article I’m going to build an app that can automatically detect the sentiment of IMDB movie reviews.

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The first thing I’ll need is a dataset with thousands of movie reviews, correctly labelled as having positive of negative sentiment.

The Kaggle IMDB dataset has exactly what I need. It’s a collection of 50,000 highly polarized movie reviews with exactly 50% positive and 50% negative reviews. My job is to build an app that reads the dataset and correctly predict the sentiment of each review.

I’ll download the IMDB Movie Dataset and save the ZIP file in the project folder that I’m going to create in a few minutes. …


In the TV show Silicon Valley there’s a famous scene where Jian-Yang demonstrates the SeeFood app that can identify any kind of food in an image.

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Of course, this being Silicon Valley, there’s a catch: the app can only identify hotdogs and classifies everything else as ‘not hotdog’.

Watch the full clip below:

In this article I am going to build this same app which must be able to identify hotdogs in any image.

The easiest way to do this is to build a convolutional neural network and train it on a dataset of hotdog and not-hotdog images. …


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Spam is becoming a huge problem. Last year, 53.5% of all email traffic worldwide was due to spam messages, with the most common topics being healthcare and dating.

Imagine how much bandwidth and electricity is being wasted here!

So let’s see if we can solve this problem. In this article, I am going to build a C# app with CNTK and NET Core that can predict if any message is spam.

CNTK is Microsoft’s Cognitive Toolkit, a tensor library on par with TensorFlow. …

About

Mark Farragher

I help C# developers learn Computer Vision and Machine Learning in 6 weeks without requiring complex math or Python | https://www.machinelearningadvantage.com

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