#MachineLearning – Free Ebook [Pattern Recognition and Machine Learning] from Christopher Bishop

pattern recognition and machine learning christopher bishop

Hi !

I have already share this information on several times in face to face conversations, so I will leave a post on my blog to have the permanent reference for it.

Christopher Bishop, Technical Fellow and Laboratory Director In Microsoft Research Cambridge, UK, gives us the chance to download for free his eBook about Pattern Recognition and Machine Learning. With more than 700 pages of a highly recommended reading

Pattern Recognition and Machine Learning

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors.

Download: https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book

Greetings @ Toronto

El Bruno

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#MachineLearning – Ebook Gratis [Pattern Recognition and Machine Learning] de Christopher Bishop

pattern recognition and machine learning christopher bishop

Buenas!

Ya lo he comentado varias veces en persona, así que dejare un post en mi blog para tener la referencia lista.

Christopher Bishop (@ChrisBishopMSFT), Technical Fellow and Laboratory Director en Microsoft Research Cambridge, UK, nos presenta la posibilidad de descarga de manera gratuita en formato PDF su libro [Pattern Recognition and Machine Learning]. Son mas de 700 paginas de una lectura muy recomendable

Pattern Recognition and Machine Learning

This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of recent developments such as probabilistic graphical models and deterministic inference methods, and to emphasize a modern Bayesian perspective. It is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. This hard cover book has 738 pages in full colour, and there are 431 graded exercises (with solutions available below). Extensive support is provided for course instructors.

Descarga: https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book

Saludos @ Toronto

El Bruno

#MLNet – Looking at data in the Pipeline in version 0.7.0

Hi!

With the new changes In Machine Learning.Net with the 0.7.0 version, the ability to peek in the data while is processed in each step of the pipeline is a little more complicated. A while ago, explain how we could do this in the post [Understanding the step bystep of Hello World]. However, ML.Net now uses Lazy objects, so it is not possible to debug in this step-by-step mode.

One of the options that we have available, is detailed in the ML.Net Cookbook, in the section [How do I look at the intermediate data?] and a complete explanation can be read in [Schema comprehension in ML.NET]

Well, the machine Learning.Net team has created a series of operations that can help us with these scenarios in the class [https://github.com/dotnet/machinelearning-samples/blob/master/samples/csharp/common/ConsoleHelper.cs]. In the following example, based on the examples of my Machine Learning.Net sessions, I use these functions to display the first 4 rows of the initial data set and also to display the values created for the Features column

So far, it works 😀

Happy Coding.

Greetings @ Microsoft IoT

El Bruno

References

My Posts

#MLNet – Visualizando datos del Pipeline en la versión 0.7.0

Buenas!

Con los nuevos cambios en Machine Learning.Net con la versión 0.7.0, la capacidad dever paso a paso como los datos se procesan es un poco mas complicado. Hace untiempo, explique cómo podíamos hacer esto en el post [Understanding the step bystep of Hello World]. Sin embargo, ahora ML.Net utiliza Lazy objects, con lo que no es posible depurar en este modo paso a paso.

Una de las opciones que tenemos disponibles, se detalla en el ML.Net Cookbook, en la sección[How do I look at the intermediate data?] Y una explicación completa se puede leer en [Schema comprehension in ML.NET]

Pues bien, el equipo de Machine Learning.Net ha creado una serie de operaciones que pueden ayudarnos con estos escenarios en el repositorio de Samples en la clase [https://github.com/dotnet/machinelearning-samples/blob/master/samples/csharp/common/ConsoleHelper.cs]. En el siguiente ejemplo, basado en los ejemplos de mis sesiones de MachineLearning.Net, utilizo estas funciones para mostrar las primeras 4 filas del set de datos inicial y también para mostrar cómo se construyen los valores en la columna Features

Lo apuntare como una solución.

Happy Coding!

Saludos @ Microsoft IoT

El Bruno

References

My Posts

#Event – Materiales utilizados en la sesión [Getting Started with Machine Learning.Net & Windows Machine Learning] con el grupo de Mississauga .NET User Group

Buenas!

Momento de agradecer al amigo Obi (@ObiOberoi) y a los miembros del grupo Mississauga .NET User Group por el apasionante rato que pasamos hace unos dias hablando de Windows ML y de Machine Learning.Net.

Como es habitual, ahora es el momento de compartir slides y Source Code de los ejemplos comentados ayer.

Muchas gracias por las preguntas y por el Feedback, todos 5 estrellas!

Source Code GitHub https://github.com/elbruno/events/tree/master/2018%2011%2022%20Mississauga%20MLNet

A continuación algunos recursos que comente en durante la sesión:

Resources

Saludos @ Toronto

El Bruno

#MLNET – New version 0.7 for Machine Learning.Net (the perfect excuse to update my content for next events!)

auto auto racing automobile automotive
Photo by Pixabay on Pexels.com

Hi!

A few days ago, the Machine Learning Net. team published a new version, 0.7. I have not had time to thoroughly review the novelties of this version, however, I will update my session and demos to version 0.7 for the next session on November 22 in Mississauga

Getting Started with Machine Learning.Net & Windows Machine Learning https://www.meetup.com/MississaugaNETUG/events/255916993/ .

Cesar de la Torre and the ML.Net team have published a full post with the novelties of ML.Net 0.7 https://blogs.msdn.microsoft.com/dotnet/2018/11/08/announcing-ml-net-0-7-machine-learning-net/ and I reshare the important bullets, to be updated on my contents:

  •     Enhanced support for recommendation tasks with Matrix Factorization
  •     Enabled anomaly detection scenarios – detecting unusual events rel
  •     Improved customizability of ML pipelines
  •     x86 support
  •     NimbusML – experimental Python bindings for ML.NET
  •     Get started with ML.NET v07

And, finally, we change the address for the next event on November 14: Artificial Intelligence and Machine Learning in Azure https://www.meetup.com/The-Azure-Group-Meetup/events/bvvjnpyxpbsb/ .

Happy coding!

Greetings @ Toronto

El Bruno

References

My Posts

#MLNET – Novedades en la version 0.7 de Machine Learning.Net (la excusa perfecta para actualizar proximos eventos!)

auto auto racing automobile automotive
Photo by Pixabay on Pexels.com

Buenas!

Hace unos días se publico la version 0.7 de Machine Learning .Net. No he tenido tiempo de repasar a fondo las novedades de esta version, sin embargo, tendré actualizada mi sesión y demos a la version 0.7 para la próxima sesión el próximo 22 de Noviembre en Mississauga

Getting Started with Machine Learning.Net & Windows Machine Learning https://www.meetup.com/MississaugaNETUG/events/255916993/ .

Pues bien, Cesar De la Torre y el equipo de ML.Net han publicado un post completo con las novedades de ML.Net 0.7 https://blogs.msdn.microsoft.com/dotnet/2018/11/08/announcing-ml-net-0-7-machine-learning-net/ y me apunto los puntos importantes del post para repasar esta semana

  •     Enhanced support for recommendation tasks with Matrix Factorization
  •     Enabled anomaly detection scenarios – detecting unusual events rel
  •     Improved customizability of ML pipelines
  •     x86 support
  •     NimbusML – experimental Python bindings for ML.NET
  •     Get started with ML.NET v07

Y, por último, hemos cambiado la ubicación del evento Artificial Intelligence and Machine Learning in Azure https://www.meetup.com/The-Azure-Group-Meetup/events/bvvjnpyxpbsb/ del próximo 14 Noviembre.

Happy coding!

Saludos @ Toronto

El Bruno

References

My Posts

#MLNET – Analyzing pipeline data in Machine Learning.Net using the new API 0.6.0 (thanks LINQ!)

Hi!

The change in the way the pipelines work in the 0.6.0 version of Machine Learning.Net, also requires some changes in our code if we want to see how the data is processed during each of the pipeline’s steps. Using the example of my previous post, I will work with the following data structure.

01 mlnet age range data

On line 21, let’s create a temporary data view with the following code

That allows me to analyze the data directly in the IDE in debugging mode, or even save the dataset in a temporary file

02 MLNet temp view of pipeline data

This may be enough, however, when working with a class that has the 4 fields of my dataset, I am forcing my App to load and map all the data for each column.

In this example, the generated model to generate a prediction only needs the fields Label and Age. So, we can remove the definition of the columns Name and Gender. We will find the following error

03 mlnet missing columns while loading data

System.InvalidOperationException

  HResult=0x80131509

  Message=Column ‘Name’ not found in the data view

  Source=Microsoft.ML.Api

By default, the load process attempts to map all columns. The solution is to enable the option to ignore missing columns, as the following example shows.

04 mlnet ignore missing columns

Finally, if we work with a lot of data, we can also leverage some LINQ features to just work with a small set of rows

05 mlnet take only 3 elements

Happy coding!

Greetings @ Toronto

El Bruno

References

My Posts

#MLNET – Analizando datos de la Pipeline con la nueva version API 0.6.0 (gracias LINQ!)

Buenas!

El cambio en la forma en la que las Pipelines trabajan en la version 0.6.0 de Machine Learning.Net, también requiere que el código se modifique un poco si queremos ver como se procesan los datos durante cada uno de los pasos de la Pipeline.

Retomando el ejemplo del post anterior, voy a trabajar con la siguiente estructura de datos.

01 mlnet age range data

En la línea 21, créate una vista temporal de datos con el siguiente código

que me permitirá analizar los mismos directamente en el IDE en modo de depuración, o inclusive guardarlos en archivos temporales para estudiar los mismos

02 MLNet temp view of pipeline data

Esto puede ser suficiente, sin embargo, al trabajar con una clase tipada con los 4 campos de mi set de datos, estoy obligando a mi App a cargar y mapear todos los datos en cada columna. En este ejemplo, el modelo generado permite generar una predicción del Label a partir del campo Age. De esta forma, podemos obviar la definición de las columnas Name y Gender en la carga.

Eso sí, nos encontraremos con el siguiente error

03 mlnet missing columns while loading data

System.InvalidOperationException

  HResult=0x80131509

  Message=Column ‘Name’ not found in the data view

  Source=Microsoft.ML.Api

Ya que, por defecto, la carga de datos intenta mapear todas las columnas. La solución es habilitar la opción para ignorar las columnas faltantes, como muestra el siguiente ejemplo.

04 mlnet ignore missing columns

Finalmente, si trabajamos con muchos datos, también podemos sacar provecho de Linq para solo analizar un subgrupo de filas

05 mlnet take only 3 elements

Happy coding!

Saludos @ Toronto

El Bruno

References

My Posts

#MLNET – API improvements in the new 0.6.0 version

Hi!

A few days ago, the ML.Net team released the 0.6.0 version of Machine Learning.Net and one of the most important changes it’s on the way we use ML.Net API.

In my MLNet sessions I usually comment on a prediction scenario based on the Label of a person to see if it is a child, baby or teenager. All of this using a small set of data with information like name, age and gender.

01 mlnet age range data

As you can see from the previous image, my training data set has the columns Name, Age, Gender And Label. well, One important detail is that I also need 2 .Net classes with fields to represent the rows of My trainings Datasets and the expected Prediction.

In the 0.5.0 version of ML.Net the way of working was based on creating a pipeline with the following steps

  • Define the data model and load training data
  • Define the features and labels
  • Select a Trainer and train the model

The generated model allowed to make Predictions. The following example explains it in 20 Lines of code

With the 0.6.0 version, the way the API is used has changed a lot. The best thing at this point is to read the original post of Cesar De la Torre where he explains the novelties of this version (see references). However, I think leaving an example of the same code, adapted to version 0.6.0, will be good enough to share an idea of how flexible is the new API

The main changes

  • Lines 5 to 9, loading the initial data file for training
  • Lines 11 to 18, Defining Features and Label for training
  • Line 20, training and model creation
  • Line 22, Creating a function to make predictions
  • Lines 24 to 32, example of a prediction

The complete example available in https://github.com/elbruno/Blog/tree/master/20181011%20MLNET%200.6%20NewAPI

Happy coding!

Greetings @ Toronto

El Bruno

References

My Posts