Feb
2018 Mar Tutorials, Overviews
All (107) | Courses, Education (7) | Meetings (19) | News, Features (11) | Opinions, Interviews (25) | Top Stories, Tweets (9) | Tutorials, Overviews (32) | Webcasts & Webinars (4)
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Top 20 Deep Learning Papers, 2018 Edition
- Apr 3, 2018.
Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results. - How to make a simple bar chart in D3
- Mar 30, 2018.
D3 is a JavaScript library that continues to grow, both in terms of popularity and possibilities, capable of creating dynamic, interactive visualisations. This tutorial provides a step-by-step guide on how to create a basic bar chart in d3, populated with data from a csv file.
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A “Weird” Introduction to Deep Learning
- Mar 30, 2018.
There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction. - Using Tensorflow Object Detection to do Pixel Wise Classification
- Mar 29, 2018.
Tensorflow recently added new functionality and now we can extend the API to determine pixel by pixel location of objects of interest. So when would we need this extra granularity?
- 5 Things You Need to Know about Reinforcement Learning
- Mar 28, 2018.
With the popularity of Reinforcement Learning continuing to grow, we take a look at five things you need to know about RL.
- Understanding Feature Engineering: Deep Learning Methods for Text Data
- Mar 28, 2018.
Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.
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Exploring DeepFakes
- Mar 27, 2018.
In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications. -
Text Data Preprocessing: A Walkthrough in Python
- Mar 26, 2018.
This post will serve as a practical walkthrough of a text data preprocessing task using some common Python tools. - Is ReLU After Sigmoid Bad?
- Mar 23, 2018.
Recently [we] were analyzing how different activation functions interact among themselves, and we found that using relu after sigmoid in the last two layers worsens the performance of the model.
- Introduction to k-Nearest Neighbors
- Mar 22, 2018.
What is k-Nearest-Neighbors (kNN), some useful applications, and how it works.
- CatBoost vs. Light GBM vs. XGBoost
- Mar 22, 2018.
Who is going to win this war of predictions and on what cost? Let’s explore.
- Top 12 Essential Command Line Tools for Data Scientists
- Mar 21, 2018.
This post is a short introductory overview of 12 Unix-like operating system command line tools of value to data science tasks, and the data scientists who perform them.
- Ranking Popular Distributed Computing Packages for Data Science
- Mar 20, 2018.
We examined 140 frameworks and distributed programing packages and came up with a list of top 20 distributed computing packages useful for Data Science, based on a combination of Github, Stack Overflow, and Google results.
- Descriptive Statistics: The Mighty Dwarf of Data Science
- Mar 20, 2018.
No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.
- Multiscale Methods and Machine Learning
- Mar 19, 2018.
We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare with similar approaches.
- R Fundamentals: Building a Simple Grade Calculator
- Mar 19, 2018.
In this tutorial, we'll teach you the basics of R by building a simple grade calculator. While we do not assume any R-specific knowledge, you should be familiar with general programming concepts.
- Quick Feature Engineering with Dates Using fast.ai
- Mar 16, 2018.
The fast.ai library is a collection of supplementary wrappers for a host of popular machine learning libraries, designed to remove the necessity of writing your own functions to take care of some repetitive tasks in a machine learning workflow.
- Web Scraping with Python: Illustration with CIA World Factbook
- Mar 16, 2018.
In this article, we show how to use Python libraries and HTML parsing to extract useful information from a website and answer some important analytics questions afterwards.
- Introduction to Markov Chains
- Mar 15, 2018.
What are Markov chains, when to use them, and how they work
- A Beginner’s Guide to Data Engineering – Part II
- Mar 15, 2018.
In this post, I share more technical details on how to build good data pipelines and highlight ETL best practices. Primarily, I will use Python, Airflow, and SQL for our discussion.
- Introduction to Optimization with Genetic Algorithm
- Mar 14, 2018.
This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
- 5 Things to Know Before Rushing to Start in Data Science
- Mar 13, 2018.
Strong math understanding, computing skills, critical thinking and presentations skills provide a strong foundation for a career in Data Science.
- Predictive and Preventive Maintenance
- Mar 13, 2018.
Analytics is becoming important part of maintenance, with applications to analyzing part failures, using failure distributions to simulate product life, and determining the root cause of failures. We provide an overview of predictive maintenance, its usage and key issues to be considered.
- How to do Machine Learning Efficiently
- Mar 13, 2018.
I now believe that there is an art, or craftsmanship, to structuring machine learning work and none of the math heavy books I tended to binge on seem to mention this.
- Top 5 Best Jupyter Notebook Extensions
- Mar 13, 2018.
Check out these 5 Jupyter notebook extensions to help increase your productivity.
- Choropleth Maps in R
- Mar 12, 2018.
Choropleth maps provides a very simple and easy way to understand visualizations of a measurement across different geographical areas, be it states or countries.
- Text Processing in R
- Mar 9, 2018.
There are good reasons to want to use R for text processing, namely that we can do it, and that we can fit it in with the rest of our analyses. Furthermore, there is a lot of very active development going on in the R text analysis community right now.
- 5 Things to Know About Machine Learning
- Mar 7, 2018.
This post will point out 5 thing to know about machine learning, 5 things which you may not know, may not have been aware of, or may have once known and now forgotten.
- Time Series for Dummies – The 3 Step Process
- Mar 5, 2018.
Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.
- Data Science in Fashion
- Mar 2, 2018.
Fashion industry is an extremely competitive and dynamic market. Trends and styles change with the blink of an eye. Data Science can be used here on historical data to predict the trends which will be “Hot” hence potentially saving a lot of time and money.
- Data Science for Javascript Developers
- Mar 2, 2018.
The goal of this tutorial is to open the door to data science programming using Javascript. This tutorial is intended for Javascript programmers without any data science experience.
- Is Google Tensorflow Object Detection API the Easiest Way to Implement Image Recognition?
- Mar 1, 2018.
There are many different ways to do image recognition. Google recently released a new Tensorflow Object Detection API to give computer vision everywhere a boost.
How Much Math do you need in Data Science?
Learning by Forgetting: Deep Neural Networks and the Jennifer Aniston Neuron