Dealing with Unbalanced Classes, SVMs, Random Forests®, and Decision Trees in Python
An overview of dealing with unbalanced classes, and implementing SVMs, Random Forests, and Decision Trees in Python.
on Apr 29, 2016 in Balancing Classes, Decision Trees, Precision, Python, Recall, Support Vector Machines, SVM, Unbalanced
How to Remove Duplicates in Large Datasets
Dealing with huge datasets can be tricky, especially the data cleaning process. One of such processing is deduplication, find out how you can solve this using the statistical techniques.
on Apr 27, 2016 in CleverTap, Data Cleaning, Data Preparation
Deep Learning in Neural Networks: An Overview
This post summarizes Schmidhuber's nowclassic (and still relevant) 35 page summary of 900 deep learning papers, giving an overview of the state of deep learning as of 2014. A great introduction to a great paper!
on Apr 26, 2016 in Academics, Jurgen Schmidhuber, Neural Networks
Top 10 IPython Notebook Tutorials for Data Science and Machine Learning
A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.
on Apr 22, 2016 in Data Science, Deep Learning, GitHub, IPython, Machine Learning, Python, Sebastian Raschka, TensorFlow
Holding Your Hand Like a Small Child Through a Neural Network – Part 2
The second of 2 posts expanding upon a nowclassic neural network blog post and demonstration, guiding the reader through the workings of a simple neural network.
on Apr 21, 2016 in Neural Networks
Holding Your Hand Like a Small Child Through a Neural Network – Part 1
The first part of this 2 part series expands upon a nowclassic neural network blog post and demonstration, guiding the reader through the foundational building blocks of a simple neural network.
on Apr 20, 2016 in Neural Networks
Comprehensive Guide to Learning Python for Data Analysis and Data Science
Want to make a career change to Data Science using python? Well learning anything on your own can be a challenge & a little guidance could be a great help, that is exactly what this article will provide you with.
on Apr 20, 2016 in Data Analysis, Data Science Education, DataCamp, Python
Deep Learning for Chatbots, Part 1 – Introduction
The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.
on Apr 19, 2016 in Chatbot, Deep Learning, Siri
Uplift Modeling Opportunities at PAW Chicago, June 2023, 2016, and PAW New York
At Chicago's Predictive Analytics World for Business conference, June 2023, 2016, and at New York’s PAW Business conference, uplift modeling will be covered in eight ways: across keynotes, sessions, and an article by PAW founder Eric Siegel. KDnuggets subscribers enjoy $150 off!
on Apr 19, 2016 in Chicago, IL, New York City, NY, PAW, Predictive Analytics World, Uplift Modeling
The MBA Data Science Toolkit: 8 resources to go from the spreadsheet to the command line
A great guide for the MBA, or any relatively nontechnical convert, for getting comfortable with the command line and other technical skills required to excel in data science.
on Apr 18, 2016 in GitHub, Haskell, Machine Learning, Python, R, SQL
Association Rules and the Apriori Algorithm: A Tutorial
A great and clearlypresented tutorial on the concepts of association rules and the Apriori algorithm, and their roles in market basket analysis.
on Apr 14, 2016 in Algobeans, Annalyn Ng, Apriori, Association Rules
How to Grow Your Own Data Scientists
How Zynga is “home growing” its own data science talent from the inside, by retraining some of our top analysts and engineers to become data scientists.
on Apr 14, 2016 in Amy Gershkoff, Apprenticeship, Data Science Education, Data Scientist, Zynga
Regression & Correlation for Military Promotion: A Tutorial
A clear and wellwritten tutorial covering the concepts of regression and correlation, focusing on military commander promotion as a use case.
on Apr 13, 2016 in Algobeans, Correlation, Military, Regression
From Science to Data Science, a Comprehensive Guide for Transition
An indepth, multifaceted, and allaround very helpful roadmap for making the switch from 'science' to 'data science,' yet generally useful for data science beginners or anyone looking to get into data science.
on Apr 12, 2016 in Advice, Bootcamp, Data Science, Kaggle, Python, R
A Pocket Guide to Data Science
A pocket guide overview of how to get started doing data science, with a focus on the practical, and with concrete steps to take to get moving right away.
on Apr 11, 2016 in Advice, Beginners, Data Science
Deep Learning from 30,000 feet
My veryhigh level overview of Deep Learning for Delta Sky Magazine, including neurons, a conspiracy, games, amazing feats of superhuman ability, and more  appropriate for reading at 30,000 feet.
on Apr 9, 2016 in Deep Learning, Delta
Tricking Deep Learning
Deep neural networks have had remarkable success with many tasks including image recognition. Read this overview regarding deep learning trickery, and why you should be cognizant.
on Apr 8, 2016 in Deep Learning, Image Recognition, TensorFlow
Basics of GPU Computing for Data Scientists
With the rise of neural network in data science, the demand for computationally extensive machines lead to GPUs. Learn how you can get started with GPUs & algorithms which could leverage them.
on Apr 7, 2016 in Algorithms, CUDA, Data Science, GPU, NVIDIA
Deep Learning for Internet of Things Using H2O
H2O is featurerich open source machine learning platform known for its R and Spark integration and it’s ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things.
on Apr 6, 2016 in Deep Learning, H2O, Internet of Things, IoT, R
