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R Learning Path: From beginner to expert in R in 7 steps
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Latest:
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Python Regular Expressions Cheat Sheet - Apr 19, 2018.
The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it's nice to have a handy reference, so we've put together this cheat sheet to help you out! -
Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step - Apr 19, 2018.
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article. -
Deep Learning With Apache Spark: Part 1 - Apr 18, 2018.
First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more. -
Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks - Apr 17, 2018.
The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model. -
Top 10 Technology Trends of 2018 - Apr 13, 2018.
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018. -
Understanding What is Behind Sentiment Analysis – Part 1 - Apr 13, 2018.
Build your first sentiment classifier in 3 steps. -
12 Useful Things to Know About Machine Learning - Apr 12, 2018.
This is a summary of 12 key lessons that machine learning researchers and practitioners have learned include pitfalls to avoid, important issues to focus on and answers to common questions. -
Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works - Apr 11, 2018.
PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm. -
Genetic Algorithm Key Terms, Explained - Apr 10, 2018.
This article presents simple definitions for 12 genetic algorithm key terms, in order to help better introduce the concepts to newcomers. -
Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model - Apr 10, 2018.
Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words. -
Loading Terabytes of Data from Postgres into BigQuery - Apr 9, 2018.
Despite the fact that an ETL task is pretty challenging when it comes to loading Big Data, there’s still the scenario in which you can load terabytes of data from Postgres into BigQuery relatively easy and very efficiently. -
Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor - Apr 6, 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. -
Scalable Select of Random Rows in SQL - Apr 5, 2018.
Performance boosts are achieved by selecting random rows or the sampling technique. Let’s learn how to select random rows in SQL. -
Supervised vs. Unsupervised Learning - Apr 4, 2018.
Understanding the differences between the two main types of machine learning methods. -
How To Choose The Right Chart Type For Your Data - Apr 3, 2018.
The power of charts to assist in accurate interpretation is massive and that's why it is vital to select the correct type when you are trying to visualize data. -
Why You Should Start Using .npy Files More Often - Apr 3, 2018.
In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times. -
Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW) - Apr 3, 2018.
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).
March:
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Top 20 Deep Learning Papers, 2018 Edition
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
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A “Weird” Introduction to Deep Learning
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
- 5 Things You Need to Know about Reinforcement Learning
- Understanding Feature Engineering: Deep Learning Methods for Text Data
- Exploring DeepFakes
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Text Data Preprocessing: A Walkthrough in Python
This post will serve as a practical walkthrough of a text data preprocessing task using some common Python tools. - Is ReLU After Sigmoid Bad?
- Introduction to k-Nearest Neighbors
- CatBoost vs. Light GBM vs. XGBoost
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Top 12 Essential Command Line Tools for Data Scientists
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
- Descriptive Statistics: The Mighty Dwarf of Data Science
- Multiscale Methods and Machine Learning
- R Fundamentals: Building a Simple Grade Calculator
- Quick Feature Engineering with Dates Using fast.ai
- Web Scraping with Python: Illustration with CIA World Factbook
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Introduction to Markov Chains
What are Markov chains, when to use them, and how they work
- A Beginner’s Guide to Data Engineering – Part II
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Introduction to Optimization with Genetic Algorithm
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
- Predictive and Preventive Maintenance
- How to do Machine Learning Efficiently
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Top 5 Best Jupyter Notebook Extensions
Check out these 5 Jupyter notebook extensions to help increase your productivity.
- Choropleth Maps in R
- Text Processing in R
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5 Things to Know About Machine Learning
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.
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Time Series for Dummies – The 3 Step Process
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.
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Data Science in Fashion
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
- Is Google Tensorflow Object Detection API the Easiest Way to Implement Image Recognition?