Tag: tensorflow

Machine Learning Algorithms – Second Edition

Artificial Intelligence, Books, Convnet, Data Science, Deep Learning, Keras, Machine Learning, Machine Learning Algorithms Addenda, Neural networks, NLP, Python, Scikit-Learn, Spark, Tensorflow, TensorflowNo Comments

The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published today and will be soon available through all channels. From the back cover: Machine learning has gained tremendous popularity for its powerful and fast predictions through large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate sufficient insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across supervised, semi-supervised, and reinforcement learning areas. Once the core concepts of an algorithm have been exposed, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component […]

Mastering Machine Learning Algorithms

Artificial Intelligence, Convnet, Deep Learning, Keras, Machine Learning, Machine Learning Algorithms Addenda, Neural networks, Python, Scikit-Fuzzy, Scikit-Learn, Tensorflow, TensorflowNo Comments

Today I’ve published my latest book “Mastering Machine Learning Algorithms” (in a few days it will be available on all channels). From the back cover: Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the […]

Hetero-Associative Memories for Non Experts: How “Stories” are memorized with Image-associations

Artificial Intelligence, Computational Neuroscience, Convnet, Deep Learning, Generic, Machine Learning, Neural networks, Philosophy of Mind, Tensorflow, Tensorflow4 Comments

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 years ago. It was the first time you made love to her. Now imagine a small animal (normally a prey, like a rodent) roaming around the forest and looking for food. A sound is suddenly heard and the rodent rises its head. Is it the pounding of water or a lion’s roar? We can skip the answer right now. Let’s only think about the ways an animal memory can work. Computer science drove us to think that memories must always be loss-less, efficient and organized like structured repositories. They can be split into standard-size slots […]

ML Algorithms Addendum: Fisher Information

Artificial Intelligence, Machine Learning, Machine Learning Algorithms Addenda, Python, TensorflowNo Comments

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 generating process p(x) (normally represented as a data set) with the highest possible accuracy. This condition can be expressed using the Kullback-Leibler divergence: Finding the best parameters that minimize the Kullback-Leibler divergence is equivalent to maximizing the log-likelihood log P(x|M) because p(x) doesn’t contain any reference to M. Now, a good (informal) question is: how much information p(x) can provide to simplify the task to the model? In other words, given a log-likelihood, which is its behavior next to the MLE point? Considering the following two plots, we can observe two different situations. The first […]

Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Networks

Convnet, Deep Learning, Generic, Keras, Neural networks, NLP, Python, Tensorflow64 Comments

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 with images) became a concrete possibility. As you know, a convolutional network trains its kernels so to be able to capture, initially coarse-grained features (like the orientation), and while the kernel-size decreases, more and more detailed elements (like eyes, wheels, hands and so forth). In the same way, a 1D convolution works on 1-dimensional vectors (in general they are temporal sequences), extracting pseudo-geometric features. The rationale is provided by the Word2Vec algorithm: as the vectors are “grouped” according to a semantic criterion so that two similar words have very close representations, a sequence can be […]

SVD Recommendations using Tensorflow

Machine Learning, Python, Tensorflow6 Comments

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 a very simple implementation based on Tensorflow. The first element to define is the user-item matrix. Normally this matrix associates each user with each product through a rating (0 means that no rating has been provided) or explicit feedback. This approach is effective whenever it’s possible to ask the user to rate the items, but there are many situations where this isn’t possible. In all those cases, an implicit feedback can be employed. For example: For a movie/video, it’s possible to consider how many times the user watched it and the average duration For a […]

Lossy image autoencoders with convolution and deconvolution networks in Tensorflow

Convnet, Deep Learning, Neural networks, Python, Tensorflow6 Comments

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 that processes an input matrix (image) and outputs a fixed-length code: In this model, the encoding function is implemented using a convolutional layer followed by flattening and dense layers. The code is then fed into the decoder, which reconstructs a lossy version of the original image: The decoder is implemented using a deconvolutional (separable convolution) layer with 3 filters (one per channel). The model is trained minimizing the L2 loss: For the experiment, I’ve used the CIFAR-10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html), using only the training samples (50000 32 x 32 RGB images) and the Keras wrapper: from […]

Machine Learning Algorithms

Keras, Machine Learning, Python, Scikit-Learn, TensorflowNo Comments

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 learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering. In this book you will also learn how these algorithms work and their practical implementation to resolve your problems. This book will also introduce you to the Natural Processing Language and Recommendation systems, which help you run multiple algorithms simultaneously. On completion of the book you will have mastered selecting Machine Learning algorithms for clustering, classification, or regression based on for your problem. ISBN: 9781785889622 Link […]

Keras-based Deepdream experiment based on VGG19

Convnet, Deep Learning, Keras, Machine Learning, Python, Tensorflow, Tensorflow, TheanoNo Comments

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 Pyramid and average the rescaled results of a layer with the next one. A total variation loss could be employed too, but after some experiments, I’ve preferred to remove it because of its blur effect. Some examples obtained with different settings in terms of layers and number of iterations:   It’s possible to create amazing videos by zooming into the same image. This in an example created with 1500 frames: Deepdream animation with Keras and VGG19 This video has been created using the notebook https://github.com/giuseppebonaccorso/keras_deepdream which is a Deepdream experiment based on some suggestions provided […]

Keras-based Neural Artistic Style Transfer

Artificial Intelligence, Convnet, Deep Learning, Keras, Machine Learning, Neural networks, Python, Tensorflow, TheanoNo Comments

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 the paper: arXiv:1508.06576 [cs.CV] which exploits a trained convolutional network in order to reconstruct the elements of a picture adopting the artistic style of a particular painting.