Oct
2017 Nov Tutorials, Overviews
All (102) | Courses, Education (7) | Meetings (9) | News, Features (9) | Opinions, Interviews (24) | Top Stories, Tweets (10) | Tutorials, Overviews (36) | Webcasts & Webinars (7)
- Machine Learning with Optimus on Apache Spark
- Nov 30, 2017.
The way most Machine Learning models work on Spark are not straightforward, and they need lots of feature engineering to work. That’s why we created the feature engineering section inside the Optimus Data Frame Transformer.
- Evolutionary Algorithms for Feature Selection
- Nov 29, 2017.
Feature selection is a very important technique in machine learning. In this post we discuss one of the most common optimization algorithms for multi-modal fitness landscapes - evolutionary algorithms.
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Why You Should Forget ‘for-loop’ for Data Science Code and Embrace Vectorization
- Nov 29, 2017.
Data science needs fast computation and transformation of data. NumPy objects in Python provides that advantage over regular programming constructs like for-loop. How to demonstrate it in few easy lines of code? - Natural Language Processing Library for Apache Spark – free to use
- Nov 28, 2017.
Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.
- How To Unit Test Machine Learning Code
- Nov 28, 2017.
One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.
- Analyzing the Migration of Scientific Researchers
- Nov 27, 2017.
This is a visualization of the inter- and intra-continental migration of scientific researchers based on ORCID (Open Researcher and Contributor ID) data. It is best seen as a directional sample of all researchers, and tracks their earliest/latest countries with research activities as well as their PhD countries.
- Deep Learning Specialization by Andrew Ng – 21 Lessons Learned
- Nov 24, 2017.
I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner.
- How (and Why) to Create a Good Validation Set
- Nov 24, 2017.
The definitions of training, validation, and test sets can be fairly nuanced, and the terms are sometimes inconsistently used. In the deep learning community, “test-time inference” is often used to refer to evaluating on data in production, which is not the technical definition of a test set.
- Understanding Objective Functions in Neural Networks
- Nov 23, 2017.
This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.
- Building a Wikipedia Text Corpus for Natural Language Processing
- Nov 23, 2017.
Wikipedia is a rich source of well-organized textual data, and a vast collection of knowledge. What we will do here is build a corpus from the set of English Wikipedia articles, which is freely and conveniently available online.
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A Framework for Approaching Textual Data Science Tasks
- Nov 22, 2017.
Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks. - Best Masters in Data Science and Analytics in US/Canada
- Nov 21, 2017.
Second comprehensive list of master's degrees in the US and Canada with tuition information and duration.
- Estimating an Optimal Learning Rate For a Deep Neural Network
- Nov 21, 2017.
This post describes a simple and powerful way to find a reasonable learning rate for your neural network.
- Automated Feature Engineering for Time Series Data
- Nov 20, 2017.
We introduce a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.
- Generative Adversarial Networks — Part II
- Nov 17, 2017.
Second part of this incredible overview of Generative Adversarial Networks, explaining the contributions of Deep Convolutional-GAN (DCGAN) paper.
- Top 10 Videos on Deep Learning in Python
- Nov 17, 2017.
Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!
- 8 Ways to Improve Your Data Science Skills in 2 Years
- Nov 17, 2017.
Two years. Two years is the maximum amount of time you should spend focused on your learning, education and training. That’s exactly why this guide is focused on honing the most beneficial skills in two years.
- Capsule Networks Are Shaking up AI – Here’s How to Use Them
- Nov 16, 2017.
If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today.
- PySpark SQL Cheat Sheet: Big Data in Python
- Nov 16, 2017.
PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. With Spark, you can get started with big data processing, as it has built-in modules for streaming, SQL, machine learning and graph processing.
- Basic Concepts of Feature Selection
- Nov 15, 2017.
Feature selection is a key part of data science but is it still relevant in the age of support vector machines (SVMs) and Deep Learning? Yes, absolutely. We explain why.
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The 10 Statistical Techniques Data Scientists Need to Master
- Nov 15, 2017.
The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post. - Best Online Masters in Data Science and Analytics – a comprehensive, unbiased survey
- Nov 14, 2017.
The first comprehensive and objective survey of online Masters in Analytics / Data Science, including rankings, tuition, and duration of the education program.
- Extracting Tweets With R
- Nov 14, 2017.
This article will give you a great, brief overview for extracting Tweets using R.
- Machine Learning Algorithms: Which One to Choose for Your Problem
- Nov 14, 2017.
This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.
- The Qualitative Side of Quantitative Research
- Nov 9, 2017.
Kevin and Koen may buy the same brand for the same reasons. On the other hand, they may buy the same brand for different reasons, or buy different brands for the same reasons, or even different brands for different reasons. The brands they purchase and the reasons why may vary by occasion, too.
- TensorFlow: What Parameters to Optimize?
- Nov 9, 2017.
Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.
- 7 Super Simple Steps From Idea To Successful Data Science Project
- Nov 8, 2017.
Ever had this great idea for a data science project or business? In the end you did not do it because you did not know how to make it a success? Today I am going to show you how to do it.
- Tips for Getting Started with Text Mining in R and Python
- Nov 8, 2017.
This article opens up the world of text mining in a simple and intuitive way and provides great tips to get started with text mining.
- Interpreting Machine Learning Models: An Overview
- Nov 7, 2017.
This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.
- Real World Deep Learning: Neural Networks for Smart Crops
- Nov 7, 2017.
The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural network graphs... made a very strong case in favor of CNNs for our classifier.
- Blockchain Key Terms, Explained
- Nov 3, 2017.
Need a quick glance over some important definitions associated with the Blockchain? Then consider this article your Blockchain Definitions 101!
- Want to know how Deep Learning works? Here’s a quick guide for everyone
- Nov 3, 2017.
Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.
- Process Mining with R: Introduction
- Nov 2, 2017.
In the past years, several niche tools have appeared to mine organizational business processes. In this article, we’ll show you that it is possible to get started with “process mining” using well-known data science programming languages as well.
- 3 different types of machine learning
- Nov 1, 2017.
In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
- Conjoint Analysis: A Primer
- Nov 1, 2017.
Conjoint is another of those things everyone talks about but many are confused about…
- Getting Started with Machine Learning in One Hour!
- Nov 1, 2017.
Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.
Why You Shouldn’t be a Data Science Generalist