Cada vez es más complicado seguir la actualidad del Big Data (en el enlace anterior teneis nuestra recopilación de lo más destacado publicado en el portal), por eso es muy interesante la recopilación de Data Science Central sobre el tema:
February 5, 2015
- Code for learning the Structure of Graphical Models
- PokitDok HealthGraph
- Data Wrangling with dplyr and tidyr Cheat Sheet
- Deep Learning in a Nutshell
- Do we Need Hundreds of Classifiers to Solve Real World Classificati... - PDF document
- Video: Advanced Machine Learning with scikit-learn
- Predictive Modeling with R and the caret Package
- Protovis: A Graphical Toolkit for Visualization
- R Data: Data Analysis and Visualization Using R
- How to Choose Between Learning Python or R First
- Top 50 open source web crawlers for data mining
- Year 2014 in Review as Seen by a Event Detection System
- Optimization Algorithms in Machine Learning
- Machine Learning course Video
- Course from Rice University: An Introduction to Interactive Program...
- MapReduce: Simplified Data Processing on Large Clusters
- MapReduce Online
- Distributed Hash Tables, Part I
- One Page R: A Survival Guide to Data Science with R
- Abridged List of Machine Learning Topics
- Decision Tree Algorithms – Simplified
- DataQuest - Browser-based learning for data science
- How To Implement These 5 Powerful Probability Distributions In Python
- Median Selection Subset Aggregation for Parallel Inference
- The caret Package - Short for Classification And REgression Training
- Bayesian Machine Learning on Apache Spark
- How to Visualize Website Clickstream Data
- Practical Data Science in Python
- Starting data analysis/wrangling with R - Things I wish I'd been told
- Sibyl: A System for Large Scale Machine Learning at Google - Video
- Top 77 R posts for 2014
- Implementing K-means Clustering to Classify Bank Customer
- Data Animations With Python and MoviePy
- A Young Person’s Guide to C# Bond
- Video: Advanced Machine Learning with scikit-learn
- pbdR: programming with big data in R
- 14 Best Python Pandas Features
- Deep Learning in a Nutshell
- Big Data for Predictive Machine Learning and Data Mining - Research paper, Cornell
- R Markdown - About repoducibility of research experiments
- Machine Learning Discussion Group - Deep Learning with Stanford AI Lab (Video 1 of 3)
- Univariate Distribution Relationships - 76 probability distributions
- Abridged List of Machine Learning Topics
- Deep Learning in Neural Networks: An Overview
- Using Word Clouds for Topic Modeling Results
- The Split-Apply-Combine Strategy for Data Analysis
- Open source dashboard templates
- Configuring a Linux Virtual Machine for Data Science - Step-by-step guide, with Python, R and GIT
- Do-it-yourself Crawlers vs. Crawlers as Service
- Abridged List of Machine Learning Topics
- Recommender Systems 101 – a step by step practical example in R
- Programming tools: Adventures with R
- Introductory R Presentation
- What is a Bayesian Network?
December 24, 2014
- Map-Reduce for Machine Learning on Multicore - PDF document
- A Map-Reduce Algorithm for Matrix Multiplication
- HAMA: An Efficient Matrix Computation with the Map-Reduce Framework - PDF document
- Deep Neural Networks are Easily Fooled: High Confidence Predictions...
- Video: An Overview of Deep Learning and Its Challenges for Technica...
- Representation Learning: A Review and New Perspectives
- 5 Amazingly powerful Python libraries for data science
- DIY Crawlers vs. Crawlers as Service
- 20 new data viz tools and resources of 2014
- JavaScript data visualization for R
- Controversies in the Foundations of Statistics - Research paper (1978)
- An open source repository for responsive dashboard templates
- Do we Need Hundreds of Classifiers to Solve Real World Classificati... - PDF document (MIT)
- A Dozen Informative Videos on Data Science
- An Introduction to Unsupervised Learning via Scikit Learn
- 30 data visualization tools
- 14 Best Python Pandas Features
- What do practitioners need to know about regression?
- 10 Big data and analytics tutorials in 2014 - From IBM
- Deep Neural Networks are Easily Fooled - PDF document
- What is deep learning? - PDF document
- Book: Statistics with R
- Automatically making sense of data
- Simple CSV Data Wrangling with Python
- Best Practices for Hadoop. Learn the best practices for applying ad...
- Data Science in the Statistics Curricula: Preparing Students to "Th...
- R, an Integrated Statistical Programming Environment and GIS
- Video: Introduction to Deep Learning with Python
- Resources regarding the Julia programming language
- Interpreting Confidence Intervals
- The learning behind gmail priority inbox
- Geoffrey Hinton on Deep Learning
- Python Packages For Data Mining
- Deep Learning Tutorial
- Recommender Systems (Machine Learning Summer School 2014 @ CMU)
- Tuning Machine Learning Models Using the Caret R Package
- Getting Started with Deep Learning and Python
- Hacker's guide to Neural Networks
- 2015: the Year of Big Data * - Warwick Data Science Institute
- Exercise to compare classifier performance
- 10 Tips for Better Deep Learning Models
- Running R in the Azure ML cloud
- Getting Started with Deep Learning and Python
- Foundations of Data Science by John Hopcroft & Ravindran Kannan
- Videos: 20th ACM SIGKDD Conference on Knowledge Discovery and Data ...
- Tutorial about Deep Belief Network in Python
- Cheat sheets for developers
- In-depth introduction to machine learning in 15 hours of expert videos
- The Python Tutorial
- Meta-list of data set repositories for cool data science projects
- K Means Clustering - Effect of random seed
- Demographic and lifestyle information by Zipcode - Interesting, but they don't provide education or age breakdown
- Equitability, mutual information, and the maximal information coeff... (Research paper)
- New approach to engineering analytics for deployment in streaming a... (Research paper)
- 50 Face Recognition APIs
- Prediction intervals too narrow
- Tutorial To Implement k-Nearest Neighbors in Python From Scratch
- Exercise to detect Algorithmically Generated Domain Names
- Deep Learning Tutorials
- Popularity rankings: How to do it Right
- How to Prepare Data For Machine Learning
- swirl teaches you R programming and data science interactively
- Data Science at the Command Line
- Overfitting and Machine Learning
- ADW, free software to measure semantic similarity
- In-depth introduction to machine learning in 15 hours of expert videos
- Hacker's guide to Neural Networks
- Visualizing MNIST: An Exploration of Dimensionality Reduction
Al comparar camisetas de fútbol, conviene revisar primero el uso previsto, la talla y la comodidad de la prenda. Si la búsqueda se centra en una figura concreta, camisetas de Lionel Messi conserva esa referencia sin mezclar equipos o selecciones. La decisión final resulta más clara cuando se comparan datos concretos y no solo la apariencia general.
Una comparación ordenada puede empezar por el corte, la transpiración del tejido y las instrucciones de cuidado. Para ordenar la búsqueda, camisetas de la seleccion argentina puede revisarse junto con las medidas y el tipo de ajuste preferido. El resultado es una comparación más ordenada entre comodidad, medidas y mantenimiento.
