Primeros pasos en Keras

June 20th, 2017|

First contact with Keras

June 18th, 2017|

FIRST CONTACT WITH TENSORFLOW: Slides & Code updated with TensorFlow 1.1

June 5th, 2017|

TensorFlow counterattacks to Caffe2

May 4th, 2017|

Few days ago appeared Caffe2 as a new player on the scene of Deep Learning frameworks with impressive performance scaling results on DGX-1. Tonight, Tensorflow shown the same numbers and the GitHub code to prove it yourself: My last post finished with this Q&A: "Will Caffe2 steal the supremacy that TensorFlow has right now? I do not have a crystal ball. My opinion is that It seems not easy, but it is the only one that could be a competitor for Google especially in the production arena in companies. We will see it soon, because Facebook has to hurry, the window of time to get it will be very small." Let me add: The window is about to close if Facebook does not work fast and hard!   UPDATE 9-May (thanks to Maurici Yagües): https://twitter.com/soumithchintala/status/861944937100722176  :-)       [...]

Caffe2: A new player on the scene of Deep Learning frameworks

April 25th, 2017|

Few days ago Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. They say that Caffe2 is the successor to Caffe ( really?), the deep learning framework developed by Berkeley AI Research and community contributors. Caffe2’s GitHub page describes it as “an experimental refactoring of Caffe that allows a more flexible way to organize computation.” As my readers know, when appeared TensorFlow I decided to pay attention to it because it could change the scene of DL/AI frameworks. Now, we are in the same situation, Caffe2 could change the current scene that Francesc Sastre, one of my master students,  build for his master thesis:  "Frameworks popularity evolution in GitHub" No questions, right? Facebook launched Caffe2, an open-source deep learning framework made with expression, speed, and modularity in mind. It address the bottlenecks observed [...]

New release of TensorFlow with distributed computing support

April 13th, 2016|

Few hours ago Google announced his TensorFlow 0.8 that includes distributed computing support. As we already presented in this blog, distributed TensorFlow is powered by the high-performance gRPC library, which supports training on hundreds of machines in parallel according Google post. It complements the recent announcement of Google Cloud Machine Learning, which enables us to use the Google Cloud Platform. The post also announces that they have published a distributed trainer for the Inception image classification neural network in the TensorFlow models repository. The distributed trainer also enables us to scale out training using a cluster management system like Kubernetes from Google. Furthermore, once we have trained our model, we can deploy to production and speed up inference using TensorFlow Serving on Kubernetes. Beyond distributed Inception, the 0.8 release includes new libraries for defining our own distributed models. Using the distributed trainer, they trained the Inception network to 78% accuracy [...]

Calçotada season and the new Deep Learning book

April 9th, 2016|

The calçotada season is coming to an end. Keep this excellent recipe from Tampa Bay Times for next year. Now it is time to read First contact with TensorFlow book. Already available a paper version, PDF version and Kindle version. Also this book is going to be freely available on-line in my web page (html version) next April 23th , Saint George's day  (Sant Jordi Day). This day is Barcelona's most romantic day of the year: St Jordi's is a day of Roses and Books. Come to see it! (*) I si no saps quin llibre regalar per St Jordi ...    Sant Jordi's day in all town and cities in Catalonia: #BooksAndRoses  

Aprender TensorFlow en Salamanca

March 31st, 2016|

Hace unos días unos alumnos de la Facultad de Ciencias de la USAL pertenecientes al  capítulo de la ACM se pusieron en contacto conmigo a propósito del libro TensorFlow y en especial por una de sus ilustraciones.  Uno de sus intereses es el campo de la inteligencia artificial y las redes neuronales artificiales, así que desde que fundaron la asociación han venido organizando anualmente workshops con esta temática [1][2]. Ahora organizan un seminario introductorio al aprendizaje automático con redes neuronales y TensorFlow. Me han mostrado su github que han preparado para el curso y sin duda va a ser impresionante, tratando temas  como las LSTM que no contiene mi libro. Les pregunté que debería contar a mis alumnos para animarlos a asistir a este seminario en Salamanca. Aquí va: "La verdad es que estudiar en Salamanca es toda una [...]

¡Google ofrece más Machine Learning a los desarrolladores!

March 24th, 2016|

Google acaba de anunciar en su blog nuevas herramientas de Machine Learning para  desarrolladores disponibles como un servicio más en su plataforma Google Cloud  a través de APIs. En el blog se indica que es exactamente la misma tecnología que está detrás de productos como Google Now o Google Photos, permitiendo a los desarrolladores construirse potentes modelos Machine Learning usando TensorFlow, además de ofrecer modelos preentrenados a través de Google Translate API,  Cloud Vision API o Google Cloud Speech API. Si juntamos esto con su servicio Cloud Dataproc que permite la gestión de procesos tanto de Hadoop como de Spark,  realmente los desarrollador e investigadores tenemos ahora mismo una potente y completa plataforma de procesado Big Data. Si tienen dos minutos les recomiendo este video insertado en su blog, para hacerse una idea a través de un simple robot realizado con una Raspberry Pi :

Distributed TensorFlow Has Arrived

March 13th, 2016|

The landscape of Deep Learning was impacted in November, 2015, with the release of Google's TensorFlow, what is now the most popular open source machine learning library on Github by a wide margin. Some researchers showed their dissatisfaction with the project because the lack of distributed training capabilities (because such capabilities were directly alluded to in the accompanying whitepaper's title). However, the distributed TensorFlow has arrived, few week ago [*] Google announced an update to its deep learning library and TensorFlow now supports distributed training. The distributed version of TensorFlow is supported by gRPC, which is a high performance, open source RPC framework for inter-process communication (the same protocol used by TensorFlow Serving). Remember that the second most-starred machine learning project of Github is Scikit-learn, the de [...]

Google launched TensorFlow Serving

February 18th, 2016|

Google launched TensorFlow Serving, that helps developers to take their TensorFlow machine learning models (and, even so, can be extended to serve other types of models) into production.  TensorFlow Serving is an open source serving system (written in C++) now available on GitHub under the Apache 2.0 license. What is the difference between TensorFlow and TensorFlow Serving?  While in TensorFlow is easier for the developers to build machine learning algorithms and train them for certain types of data inputs, TensorFlow Serving specializes in making these models usable in production environments.  The idea is that developers train their models using TensorFlow and then they use TensorFlow Serving’s APIs to react to input from a client. This allows developers to experiment with different models in a large scale that change over time based on real-world data, and maintain a [...]