2016 Jun Tutorials, Overviews
All (119) | Courses, Education (6) | Meetings (12) | News, Features (21) | Opinions, Interviews, Reports (23) | Software (10) | Tutorials, Overviews (40) | Webcasts & Webinars (7)
- Recursive (not Recurrent!) Neural Networks in TensorFlow - Jun 30, 2016.
Learn how to implement recursive neural networks in TensorFlow, which can be used to learn tree-like structures, or directed acyclic graphs.
- Peeking Inside Convolutional Neural Networks - Jun 29, 2016.
This post discusses using some tricks to peek inside of the neural network, and to visualize what the individual units in a layer detect.
- Mining Twitter Data with Python Part 5: Data Visualisation Basics - Jun 29, 2016.
Part 5 of this series takes on data visualization, as we look to make sense of our data and highlight interesting insights.
- Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences - Jun 27, 2016.
Part 4 of this series employs some of the lessons learned thus far to analyze tweets related to rugby matches and term co-occurrences.
- Improving Nudity Detection and NSFW Image Recognition - Jun 25, 2016.
This post discussed improvements made in a tricky machine learning classification problem: nude and/or NSFW, or not?
- Regularization in Logistic Regression: Better Fit and Better Generalization? - Jun 24, 2016.
A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.
- Doing Data Science: A Kaggle Walkthrough Part 6 – Creating a Model - Jun 24, 2016.
In the final part of this 6 part series on the process of data science, and applying it to a Kaggle competition, building the predictive models is covered, and multiple algorithms are discussed.
- Ten Simple Rules for Effective Statistical Practice: An Overview - Jun 23, 2016.
An overview of 10 simple rules to follow to ensure proper effective statistical data analysis.
- Mining Twitter Data with Python Part 3: Term Frequencies - Jun 22, 2016.
Part 3 of this 7 part series focusing on mining Twitter data discusses the analysis of term frequencies for meaningful term extraction.
- A Review of Popular Deep Learning Models - Jun 21, 2016.
This post is a concise overview of a few of the more interesting popular deep learning models to have appeared over the past year. Get up to speed and try a few of the models out for yourself.
- HPE Haven OnDemand Text Extraction API Cheat Sheet for Developers - Jun 21, 2016.
HPE Haven OnDemand provides a native API based on cURL calls, as well as numerous language-specific APIs, providing maximum flexibility for developers. This cheat sheet will cover the native and Python text extraction APIs.
- How to Compare Apples and Oranges, Part 2 – Categorical Variables - Jun 21, 2016.
In the previous article, we looked at some of the ways to compare different numerical variables. In this article, we shall look at techniques to compare categorical variables with the help of an example.
- Mining Twitter Data with Python Part 2: Text Pre-processing - Jun 20, 2016.
Part 2 of this 7 part series on mining Twitter data for a variety of use cases focuses on the pre-processing of tweet text.
- A Visual Explanation of the Back Propagation Algorithm for Neural Networks - Jun 17, 2016.
A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
- Doing Data Science: A Kaggle Walkthrough Part 5 – Adding New Data - Jun 17, 2016.
Here is part 5 of the weekly 6 part series on doing data science in the context of a Kaggle competition, which concentrates on adding in new data.
- How to Compare Apples and Oranges – Part 1 - Jun 17, 2016.
We are always told that apples and oranges can’t be compared, they are completely different things. Learn as an analyst, how you deal with such difference and make sense of it on a daily basis.
- Nutrition & Principal Component Analysis: A Tutorial - Jun 16, 2016.
A great overview of Principal Component Analysis (PCA), with an example application in the field of nutrition.
- 7 Steps to Mastering SQL for Data Science - Jun 16, 2016.
Follow these 7 steps to go from SQL data science newbie to seasoned practitioner quickly. No nonsense, just the necessities.
- Mining Twitter Data with Python Part 1: Collecting Data - Jun 15, 2016.
Part 1 of a 7 part series focusing on mining Twitter data for a variety of use cases. This first post lays the groundwork, and focuses on data collection.
- How to Select Support Vector Machine Kernels - Jun 13, 2016.
Support Vector Machine kernel selection can be tricky, and is dataset dependent. Here is some advice on how to proceed in the kernel selection process.
- Apache Spark Key Terms, Explained - Jun 13, 2016.
An overview of 13 core Apache Spark concepts, presented with focus and clarity in mind. A great beginner's overview of essential Spark terminology.
- A Brief Primer on Linear Regression – Part 2 - Jun 13, 2016.
This second part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.
- Doing Data Science: A Kaggle Walkthrough Part 4 – Data Transformation and Feature Extraction - Jun 10, 2016.
Part 4 of this fantastic 6 part series covering the process of data science, and its application to a Kaggle competition, focuses on feature extraction and data transformation.
- Build Your Own Audio/Video Analytics App With HPE Haven OnDemand – Part 2 - Jun 10, 2016.
In the conclusion to this two part tutorial, learn how to leverage HPE Haven OnDemand's Machine Learning APIs to build an audio/video analytics app with minimal time and effort.
- Top NoSQL Database Engines - Jun 10, 2016.
An overview of the top 5 NoSQL database engines in use today, including examples of key-value, column-oriented, graph, and document paradigms.
- An Introduction to Scientific Python (and a Bit of the Maths Behind It) – Matplotlib - Jun 9, 2016.
An introductory overview of Matplotlib, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.
- Cloud Computing Key Terms, Explained - Jun 9, 2016.
A concise overview of 20 core cloud computing ecosystem concepts. The focus here is on the terminology, not The Big Picture.
- Build Your Own Audio/Video Analytics App With HPE Haven OnDemand – Part 1 - Jun 9, 2016.
In this first part of a two part tutorial, learn how to leverage HPE Haven OnDemand's Machine Learning APIs to build an audio/video analytics app with minimal time and effort.
- 5 Best Practices for Big Data Security - Jun 9, 2016.
Lack of data security can not only result in financial losses, but may also damage the reputation of organizations. Take a look at some of the most important data security best practices that can reduce the risks associated with analyzing a massive amount of data.
- Data Science of Variable Selection: A Review - Jun 7, 2016.
There are as many approaches to selecting features as there are statisticians since every statistician and their sibling has a POV or a paper on the subject. This is an overview of some of these approaches.
- Open Source Machine Learning Degree - Jun 6, 2016.
A set of free resources for learning machine learning, inspired by similar open source degree resources. Find links to books and book-length lecture notes for study.
- A Brief Primer on Linear Regression – Part 1 - Jun 6, 2016.
This introduction to linear regression discusses a simple linear regression model with one predictor variable, and then extends it to the multiple linear regression model with at least two predictors.
- Building Data Systems: What Do You Need? - Jun 3, 2016.
This post shares some insight gained through years of building data-powered products, and discusses the capabilities you need to have in place in order to successfully build and maintain data systems and data infrastructure.
-
What is the Difference Between Deep Learning and “Regular” Machine Learning? - Jun 3, 2016.
Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning. - Doing Data Science: A Kaggle Walkthrough Part 3 – Cleaning Data - Jun 3, 2016.
This is part three in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. In this episode, data cleaning and preparation is covered.
- Understanding Modern Data Systems - Jun 2, 2016.
A look at the four characteristics that differentiate data infrastructure development from traditional development, and the key issues to look out for.
- Do You Need Big Data or Smart Data? Part 2 - Jun 2, 2016.
It can be easy to get carried away with the deluge of big data and to rely on its abundance to deliver better models. However, use of data without context and objective could prove counterproductive; contextual and objective driven samples from the large volume and variety of data can be effective tools.
- How to Build Your Own Deep Learning Box - Jun 2, 2016.
Want to build an affordable deep learning box and get all the required software installed? Read on for a proper overview.
- An Introduction to Scientific Python (and a Bit of the Maths Behind It) – NumPy - Jun 1, 2016.
An introductory overview of NumPy, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.
- Do You Need Big Data or Smart Data? Part 1 - Jun 1, 2016.
Analyzing Big Data without paying attention to its characteristics and objective can be detrimental, the fix for which can be correct and effective sampling. Read on to transform your Big Data to Smart Data.
What is the Difference Between Deep Learning and “Regular” Machine Learning?
May
