Think about walking along a beach. The radio of a small kiosk-bar is turned-on and a local DJ announces an 80’s song. Immediately, the image of a car comes to your mind. It’s your first car, a second-hand blue spider. While listening to the same song, you drove your girlfriend to the beach, about 25 … [Read more…]
Fisher Information, named after the statistician Ronald Fisher, is a very powerful tool in both Bayesian Statistics and Machine Learning. To understand its “philosophical” meaning, it’s useful to think about a simple classification task, where our task is to find a model (characterized by a set of parameters) that is able to reproduce the data … [Read more…]
Fork Word2Vec (https://code.google.com/archive/p/word2vec/) offers a very interesting alternative to classical NLP based on term-frequency matrices. In particular, as each word is embedded into a high-dimensional vector, it’s possible to consider a sentence like a sequence of points that determine an implicit geometry. For this reason, the idea of considering 1D convolutional classifiers (usually very efficient … [Read more…]
Recommendation system based on the user-item matrix factorization have become more and more important thanks to powerful and distributable algorithms like ALS, but sometimes the number of users and/or items is not so huge and the computation can be done using directly a SVD (Singular Value Decomposition) algorithm. In this post, we’re going to discuss … [Read more…]
Fork Autoencoders are a very interesting deep learning application because they allow a consistent dimensionality reduction of an entire dataset with a controllable loss level. The Jupyter notebook for this small project is available on the Github repository: https://github.com/giuseppebonaccorso/lossy_image_autoencoder. The structure of a generic autoencoder is represented in the following figure: The encoder is a function … [Read more…]
My latest machine learning book has been published and will be available during the last week of July. From the back cover: In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised … [Read more…]
Fork I’ve just published a repository (https://github.com/giuseppebonaccorso/keras_deepdream) with a Jupyter notebook containing a Deepdream (https://github.com/google/deepdream) experiment created with Keras and a pre-trained VGG19 convolutional network. The experiment (which is a work in progress) is based on some suggestions provided by the Deepdream team in this blog post but works in a slightly different way. I use a Gaussian … [Read more…]
Fork I’ve just moved my Keras-based Neural Artistic Style Transfer GIST to a dedicated repository: https://github.com/giuseppebonaccorso/Neural_Artistic_Style_Transfer. Please refer always to it because the GIST is not more maintained. See also: Neural artistic style transfer experiments with Keras – Giuseppe Bonaccorso Artistic style transfer using neural networks is a technique proposed by Gatys, Ecker and Bethge in … [Read more…]