2017 Oct Tutorials, Overviews

• 7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
  Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.

  7 Steps, Convolutional Neural Networks, Deep Learning, Keras, Logistic Regression, LSTM, Machine Learning, Neural Networks, Python, Recurrent Neural Networks

• Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning - Oct 28, 2017.
  This is a short post for beginners learning neural networks, covering several essential neural networks concepts.

  Beginners, Deep Learning, Neural Networks

• Updates & Upserts in Hadoop Ecosystem with Apache Kudu - Oct 27, 2017.
  A new open source Apache Hadoop ecosystem project, Apache Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

  Apache, Big Data, Data Management, Hadoop, Java, NoSQL

• Hello, World: Building an AI that understands the world through video - Oct 26, 2017.
  At TwentyBN, we have created the world’s first AI technology that shows an awareness of its environment and of the actions occurring within it. Our system observes the world through live video and automatically interprets the unfolding visual scene.

  AI, Deep Learning, Neural Networks, Video recognition

• Density Based Spatial Clustering of Applications with Noise (DBSCAN) - Oct 26, 2017.
  DBSCAN clustering can identify outliers, observations which won’t belong to any cluster. Since DBSCAN clustering identifies the number of clusters as well, it is very useful with unsupervised learning of the data when we don’t know how many clusters could be there in the data.

  Algorithms, Clustering, DBSCAN, Machine Learning

• Neural Network Foundations, Explained: Updating Weights with Gradient Descent & Backpropagation - Oct 25, 2017.
  In neural networks, connection weights are adjusted in order to help reconcile the differences between the actual and predicted outcomes for subsequent forward passes. But how, exactly, do these weights get adjusted?

  Backpropagation, Explained, Gradient Descent, Neural Networks

• No order left behind; no shopper left idle. - Oct 25, 2017.
  This post is about how we use Monte Carlo simulations to balance supply and demand in a rapidly growing, high-variance marketplace.

  Simulation, Variance

• Top 10 Machine Learning with R Videos - Oct 24, 2017.
  A complete video guide to Machine Learning in R! This great compilation of tutorials and lectures is an amazing recipe to start developing your own Machine Learning projects.

  Algorithms, Clustering, K-nearest neighbors, Machine Learning, PCA, R, Text Mining, Top 10, Youtube

• TensorFlow: Building Feed-Forward Neural Networks Step-by-Step - Oct 23, 2017.
  This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.

  Deep Learning, Neural Networks, TensorFlow

• Top 10 Machine Learning Algorithms for Beginners - Oct 20, 2017.
  A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
   
   

  Adaboost, Algorithms, Apriori, Bagging, Beginners, Boosting, Decision Trees, Ensemble Methods, Explained, K-means, K-nearest neighbors, Linear Regression, Logistic Regression, Machine Learning, Naive Bayes, PCA, Top 10

• 5 Free Resources for Furthering Your Understanding of Deep Learning - Oct 20, 2017.
  This post includes 5 specific video-based options for furthering your understanding of neural networks and deep learning, collectively consisting of many, many hours of insights.

  Andrew Ng, Deep Learning, Neural Networks, NIPS, Summer School

• 7 Types of Artificial Neural Networks for Natural Language Processing - Oct 19, 2017.
  What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.

  AI, Deep Learning, Natural Language Processing, Neural Networks

• 7 Techniques to Visualize Geospatial Data - Oct 19, 2017.
  In this article, we explore 7 interesting yet simple techniques to visualize geospatial data that will help you visualize your data better.

  Data Visualization, Geospatial

• Random Forests®, Explained - Oct 17, 2017.
  Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.

  Algorithms, CART, Decision Trees, Ensemble Methods, Explained, Machine Learning, random forests algorithm, Salford Systems

• How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool - Oct 16, 2017.
  In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.

  Deepak Agarwal, LinkedIn, Machine Learning, Recommendation, Recommender Systems

• Best practices of orchestrating Python and R code in ML projects - Oct 12, 2017.
  Instead of arguing about Python vs R I will examine the best practices of integrating both languages in one data science project.

  DVC, Machine Learning, Python, Python vs R, R

• Edge Analytics – What, Why, When, Who, Where, How? - Oct 11, 2017.
  Edge analytics is the collection, processing, and analysis of data at the edge of a network either at or close to a sensor, a network switch or some other connected device.

  Analytics, Edge Analytics, IoT, Sensors

• Learn Generalized Linear Models (GLM) using R - Oct 11, 2017.
  In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.

  Generalized Linear Models, Linear Regression, Logistic Regression, Machine Learning, R, Regression

• An opinionated Data Science Toolbox in R from Hadley Wickham, tidyverse - Oct 10, 2017.
  Get your productivity boosted with Hadley Wickham's powerful R package, tidyverse. It has all you need to start developing your own data science workflows.

  Data Analysis, Data Science, Data Science Platform, Data Science Tools, Hadley Wickham, R, Tidyverse

• A Quick Guide to Fake News Detection on Social Media - Oct 10, 2017.
  Fake news is an important issue on social media. This article provides an overview of fake news characterization and detection in Data Science and Machine Learning research.

  Fake News, Machine Learning, Social Media

• The 5 Common Mistakes That Lead to Bad Data Visualization - Oct 10, 2017.
  Here are 5 common mistakes that lead to bad data visualization. Avoid these to get the most out of your data visualizations.

  Data Visualization, Mistakes

• How to Choose a Data Science Job - Oct 6, 2017.
  All Data Scientists worth their salt should know the importance of working with facts rather than hunches. That’s why in the following article we’ll throw light on how five emerging roles yield a proven value that companies cannot ignore.

  Advice, Career, Data Science, Data Scientist

• Deep Learning for Object Detection: A Comprehensive Review - Oct 6, 2017.
  By the end of this post, we will hopefully have gained an understanding of how deep learning is applied to object detection, and how these object detection models both inspire and diverge from one another.

  Convolutional Neural Networks, Deep Learning, Image Recognition, Neural Networks

• A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs) - Oct 5, 2017.
  Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.

  Finance, LSTM, Neural Networks, Recurrent Neural Networks, Statsbot

• Applied Data Science: Solving a Predictive Maintenance Business Problem - Oct 5, 2017.
  The use case involved is to predict the end life of large industrial batteries, which falls under the genre of use cases called preventive maintenance use cases.

  Business, Data Science, Predictive Maintenance

• Using Machine Learning to Predict and Explain Employee Attrition - Oct 4, 2017.
  Employee attrition (churn) is a major cost to an organization. We recently used two new techniques to predict and explain employee turnover: automated ML with H2O and variable importance analysis with LIME.

  Churn, H2O, HR, Machine Learning, Predictive Analytics, Workforce Analytics

• Neural Networks: Innumerable Architectures, One Fundamental Idea - Oct 4, 2017.
  At the end of this post, you’ll be able to implement a neural network to identify handwritten digits using the MNIST dataset and have a rough time idea about how to build your own neural networks.

  AI, Machine Learning, Neural Networks, Python, TensorFlow

• XGBoost, a Top Machine Learning Method on Kaggle, Explained - Oct 3, 2017.
  Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.

  Algorithms, Data Science, Explained, Kaggle, Machine Learning

• Understanding Machine Learning Algorithms - Oct 3, 2017.
  Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.

  Algorithms, Ensemble Methods, Gradient Boosting, Machine Learning, Neural Networks, Predictive Analytics, random forests algorithm, SVM