Category: Keras

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 […]

Getting Started with NLP and Deep Learning with Python

Artificial Intelligence, Deep Learning, Keras, Machine Learning, Neural networks, NLP, Python, Scikit-Learn, TensorflowNo Comments

A tutorial video (2 hours) derived from the book Machine Learning Algorithms has been released: Getting Started with NLP and Deep Learning with Python. From the notes: As the amount of data continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine Learning applications are everywhere, from self-driving cars to spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of Big Data and Data Science. The main challenge is how to transform data into actionable knowledge. In this course, you’ll be introduced to the Natural Processing Language and Recommendation Systems, which help you run multiple algorithms simultaneously. Also, you’ll learn about Deep learning and TensorFlow. Finally, you’ll see how to create an Ml architecture. ISBN: 9781789138894 Link to the publisher page: https://www.packtpub.com/big-data-and-business-intelligence/getting-started-nlp-and-deep-learning-python-video Machine Learning Algorithms – Giuseppe Bonaccorso My […]

An annotated path to start with Machine Learning

Artificial Intelligence, Deep Learning, Generic, Keras, Machine Learning, Neural networks, Python, Scikit-Learn, Tensorflow, Theano2 Comments

“Do not worry about your difficulties in Mathematics. I can assure you mine are still greater.” (A. Einstein)   Machine Learning is becoming more and more widespread and, day after day, new computer scientists and engineers begin their long jump into this wonderful world. Unfortunately, the number of theories, algorithms, applications, papers, books, videos and so forth is so huge to disorient whoever hasn’t a clear picture of what he wants/needs to learn to improve his/her skills. In this short post, I wanted to share my experiences, suggesting a feasible path to learn quickly the essential concepts and being ready to go deeper the most complex topics. Of course, this is only a personal proposal: every student can choose to dedicate more attention to some topics which are more interesting based on his/her experience. Prerequisites Machine Learning is based on Mathematics. It’s not an optional, theoretical approach: it’s a fundamental pillar […]

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 […]

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 […]

Seq2Seq experiment with mathematical expressions

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

After reading the article “How to Learn to Add Numbers with seq2seq Recurrent Neural Networks” by Jason Brownlee (that I suggest reading before going on), I’ve decided to try an experiment with more complex expressions like: -(10+5) or 4+ -2, etc. The code (with some extra information) is published on the GIST: https://goo.gl/ZmH6Tf, where there are also some test results. Unfortunately, the results are not extraordinary and there are still many errors, however, I think it depends on the size of the dataset and on the limited ability to generalize that Seq2Seq networks show. I’m working on an enhanced version, to allow a bit more generalization. Complete Python script (Keras 2 with Theano/Tensorflow is needed, moreover I’ve used Scikit-Learn for binarization): View the code on Gist. See also: Hopfield Networks addendum: Brain-State-in-a-Box model – Giuseppe Bonaccorso The Brain-State-in-a-Box is neural model proposed by Anderson, Silverstein, Ritz and Jones in 1977, that […]

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.

BBC News classification algorithm comparison

Keras, Machine Learning, Neural networks, NLP, Scikit-Learn, TheanoNo Comments

Fork BBC News dataset (available for download in Insight Project Resources website) is made up of 2225 newslines classified into 5 categories (Politics, Sport, Entertainment, Tech, Business) and, similarly to Reuters-21578, it can be adopted in order to test both the efficacy and the efficiency of different classification strategies. In the repository: https://github.com/giuseppebonaccorso/bbc_news_classification_comparison, I’ve committed a Jupyter (IPython) notebook (based on Scikit-Learn, NLTK, Gensim, Keras (with Theano or Tensorflow)) where I’ve collected some experiments aimed at comparing four different algorithms: Multinomial Naive-Bayes with Count (TF) vectorizer Multinomial Naive-Bayes with TF-IDF vectorizer SVM (linear and kernelized) with Doc2Vec (Gensim-based) vectorization MLP (Keras-based) with Doc2Vec vectorization (Every experiment has been performed on tokens without stop-words and processed with WordNet lemmatization). As expected (thanks to several research projects – see references for further information), Naive-Bayes performs even better than the other strategies, in particular comparing its simplicity and naiveness with the complexity of SVM and neural […]