Machine Learning
«H2O has been the driver for building models at scale. We are talking about billions of claims. You can’t do this with standard off the shelf open source techniques». (H2o.ai).
AutoML: a introduction tutorial about H2O Driverless AI Leer más »
A great selection of resources on learning deeplearning by Lex Fridman from the Massachusetts Institute of Technology (MIT).
Resources on learning deeplearning from the Massachusetts Institute of Technology Leer más »
Phyton’s most notable points are:
-Is a great library ecosystem (Scikit-learn, Pandas, Matplotlib, NLTK, Scikit-image, PyBrain, Caffe, StatsModels, TensorFlow, Keras, etc).
-Growing popularity.
-Has a low entry barrier, has flexibility, is a platform independence, has readability, good visualization options, good community support and growing popularity.
Machine learning in Python: the top programming language Leer más »
Although most concepts are relatively simple, there are many of them, and they interact with each other in unobvious ways, which is a major challenge of neural networks. But you can learn all important neural network concepts by running and examining the code in this book, with complete example programs for the three major types of neural network problems.
Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications.
Cybersecurity attacks are growing both in frequency and sophistication over the years. This increasing sophistication and complexity call for more advancement and continuous innovation in defensive strategies. Traditional methods of intrusion detection and deep packet inspection, while still largely used
and recommended, are no longer sufficient to meet the demands of growing security threats.
Cyber Attack Detection thanks to Machine Learning Algorithms Leer más »
«dabl tries to reduce the turnaround time required for a quick baseline estimate of a supervised learning problem. It does so by automating the task of iterating through different techniques of data preprocessing, feature engineering, parameter tuning and model building to generate efficacious baseline models».
Machine Learning with dabl ( 0.1.8) Leer más »
The combined impact of new computing resources and techniques with an increasing avalanche of large datasets, is transforming many research areas and may lead to technological breakthroughs that can be used by billions of people. In the recent years, Machine Learning and especially its subfield Deep Learning have seen impressive advances. Techniques developed within these two fields are now able to analyze and learn from huge amounts of real world examples in a disparate formats. While the number of Machine Learning algorithms is extensive and growing, their implementations through frameworks and libraries is also extensive and growing too.
This book is all about applying machine learning solutions for real practical use cases. This means the core focus is on outlining how to use machine learning in a simple way so you can benefit of this powerful technology.
Machine learning is an exciting and powerful technology. The continuous use and growth of machine learning technology opens new opportunities. This great technology should available to use for everyone. This means that everyone should be able to learn, play and create great applications using machine learning technology.
Free and Open Machine Learning Documentation Release 1.0.1 Leer más »
(Deep Neural Networks in Geophysics) Esta tesis investiga las propiedades fundamentales de las redes neuronales en aplicaciones geofísicas. Incluye la reutilización de redes neuronales entrenadas, que son excelentes para identificar imágenes y aplicarlas para identificar capas de rocas y eventos geológicos en imágenes geofísicas. Esta tesis profundiza para evaluar si la teoría de incluir información específica de
Aprendizaje automático en análisis de datos sísmicos 4D Leer más »