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• Interpretability is crucial for trusting AI and machine learning - Nov 30, 2018.
  
  We explain what exactly interpretability is and why it is so important, focusing on its use for data scientists, end users and regulators.

  AI, Explainable AI, Explanation, Interpretability, Machine Learning, Trust

• A Complete Guide to Choosing the Best Machine Learning Course - Nov 30, 2018.
  
  A collection of the best courses covering machine learning concepts and techniques, including supervised and unsupervised learning, and hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer.

  Career, Machine Learning, Online Education, Simplilearn

• Deep Learning for the Masses (… and The Semantic Layer) - Nov 30, 2018.
  
  Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.

  Pages: 1 2

  AI, Deep Learning, Machine Learning, Semantic Analysis

• Variational Autoencoders Explained in Detail - Nov 30, 2018.
  
  We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.

  Autoencoder, Deep Learning, Machine Learning, MNIST, TensorFlow

• Top KDnuggets tweets, Nov 21-27: Intro to #DataScience for Managers – a mindmap; An Introduction to #AI - Nov 28, 2018.
  
  Also: An Introduction to #AI; Intuitively Understanding Convolutions for #DeepLearning; 10 Free Must-See Courses for Machine Learning and Data Science.

  Convolutional Neural Networks, Data Science, Manager, Top tweets

• 8 Reasons to Take Data Analytics Certification Courses - Nov 28, 2018.
  
  We outline some of the benefits of taking data analytics classes, including the huge job opportunities, the current gap in the market, the salary aspect, the flexibility of working in any sector, and more.

  Certification, Data Analytics, Simplilearn

• Sales Forecasting Using Facebook’s Prophet - Nov 28, 2018.
  
  In this tutorial we’ll use Prophet, a package developed by Facebook to show how one can achieve this.

  Facebook, Python, Sales, Time Series

• Deep Learning Cheat Sheets - Nov 28, 2018.
  
  Check out this collection of high-quality deep learning cheat sheets, filled with valuable, concise information on a variety of neural network-related topics.

  Cheat Sheet, Deep Learning, Neural Networks

• How to Engineer Your Way Out of Slow Models - Nov 27, 2018.
  
  We describe how we handle performance issues with our deep learning models, including how to find subgraphs that take a lot of calculation time and how to extract these into a caching mechanism.

  Deep Learning, Scalability

• Bringing Machine Learning Research to Product Commercialization - Nov 27, 2018.
  
  In this blog post I want to share some of the insights into the differences between academia and industry when applying deep learning to real-world problems as we experienced them at Merantix over the last two years.

  Academics, Machine Learning, Products, Research

• My secret sauce to be in top 2% of a Kaggle competition - Nov 26, 2018.
  
  A collection of top tips on ways to explore features and build better machine learning models, including feature engineering, identifying noisy features, leakage detection, model monitoring, and more.

  Competition, Data Science, Kaggle

• Data Science Strategy Safari: Aligning Data Science Strategy to Org Strategy - Nov 26, 2018.
  
  The title of this post is derived by drawing inspiration from Mintzberg’s seminal work. In this post, I am attempting to take you on a safari through the data science strategy formulation process.

  Business, Data Science, Strategy

• Top 5 domains Big Data analytics helps to transform - Nov 23, 2018.
  
  Big data analytics gives a competitive advantage to companies across many industries, especially, financial services, e-commerce, aviation, transportation, logistics, and energy. It enables to reduce downtime, mitigate risks, cut costs, and improve performance.

  Aviation, Big Data, Big Data Analytics, Credit Risk, Data Analytics, Ecommerce, Finance, Security

• Intro to Data Science for Managers - Nov 23, 2018.
  
  This mindmap contains a condensed introduction to the key data science concepts and techniques that have revolutionized the business landscape and became essential for making beneficial data-driven decisions

  Data Science, Manager, Visualization

• 6 Goals Every Wannabe Data Scientist Should Make for 2019 - Nov 22, 2018.
  
  Looking to embark on a new path as a data scientist? That goal may be worthy, but it's essential for people to also set goals for 2019 that will help them get closer to that broader aim.

  Advice, Career, Data Science

• Cartoon: Thanksgiving, Big Data, and Turkey Data Science. - Nov 22, 2018.
  
  A classic KDnuggets Thanksgiving cartoon examines the predicament of one group of fowl Data Scientists.

  Cartoon, Data Science, Thanksgiving

• An Introduction to AI - Nov 21, 2018.
  
  We provide an introduction to AI key terminologies and methodologies, covering both Machine Learning and Deep Learning, with an extensive list including Narrow AI, Super Intelligence, Classic Artificial Intelligence, and more.

  AI, Beginners

• Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices - Nov 21, 2018.
  
  LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.

  Finance, Keras, LSTM, Neural Networks, Stocks

• Word Morphing – an original idea - Nov 20, 2018.
  
  In this post, we describe how to utilise word2vec's embeddings and A* search algorithm to morph between words.

  NLP, Python, Text Classification

• How Important is that Machine Learning Model be Understandable? We analyze poll results - Nov 19, 2018.
  
  About 85% of respondents said it was always or frequently important that Machine Learning model be understandable. This was is especially important for academic researchers, and surprisingly more in US/Canada than in Europe or Asia.

  Asia, Europe, Explainable AI, Explanation, GDPR, Machine Learning, Poll, USA

• The Big Data Game Board™ - Nov 19, 2018.
  
  Move aside “Monopoly,” “Risk,” and “Snail Race!” Time to teach the youth of the world of an important, career-advancing game: how to leverage data and analytics to change your life! Introducing the “Big Data Game Board™”!

  Big Data, Data Science, Games

• Anticipating the next move in data science – my interview with Thomson Reuters - Nov 17, 2018.
  
  Like chess, Big Data is a combination of science, art and play; Gregory Piatetsky-Shapiro of KDnuggets helps data devotees discover winning moves - my Thomson Reuters interview.

  About Gregory Piatetsky, AI, Automation, Chess, Humans vs Machines, Self-Driving Car, Thomson Reuters

• Using Uncertainty to Interpret your Model - Nov 16, 2018.
  
  We outline why you should care about uncertainty and discuss the different types, including model, data and measurement uncertainty and what different purposes these all serve.

  Explainable AI, Interpretability, Taboola, Uncertainty

• Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU - Nov 15, 2018.
  
  A detailed comparison of the best places to train your deep learning model for the lowest cost and hassle, including AWS, Google, Paperspace, vast.ai, and more.

  Cloud Computing, Deep Learning, GPU, TPU

• Mastering The New Generation of Gradient Boosting - Nov 15, 2018.
  
  Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.

  Boosting, Gradient Boosting, Machine Learning, Python

• What is the Best Python IDE for Data Science? - Nov 14, 2018.
  
  Before you start learning Python, choose the IDE that suits you the best. We examine many available tools, their pros and cons, and suggest how to choose the best Python IDE for you.

  Data Science, IDE, Jupyter, Programming, Python

• [Download] Real-Life ML Examples + Notebooks - Nov 13, 2018.
  
  In this eBook, we will walk you through four Machine Learning use cases on Databricks: Loan Risk Use Case; Advertising Analytics & Prediction Use Case; Market Basket Analysis Problem at Scale; Suspicious Behavior Identification in Video Use Case. Get your copy now!

  Databricks, ebook, Jupyter, Machine Learning, Use Cases

• The 5 Basic Statistics Concepts Data Scientists Need to Know - Nov 13, 2018.
  
  Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!

  Data Science, Data Scientist, Statistics

• Self-Service Analytics and Operationalization – Why You Need Both - Nov 12, 2018.
  
  Get the guidebook / whitepaper for a look at how today's top data-driven companies scale their advanced analytics & machine learning efforts.

  Analytics, Data Science, Dataiku, Deployment, Self-service

• Healthcare Analytics Made Simple - Nov 12, 2018.
  
  Finally, a book on Python healthcare machine learning techniques is here! Healthcare Analytics Made Simple does just what the title says: it makes healthcare data science simple and approachable for everyone.

  Analytics, Book, Healthcare, Pandas, Python

• To get hired as a data scientist, don’t follow the herd - Nov 12, 2018.
  
  Key tips, including advice on how to step out of your comfort zone and sometimes overlooked important skills that will impress employers. Check also the audio version with additional advice.

  Data Science Skills, Data Scientist, Hiring

• The Long Tail of Medical Data - Nov 12, 2018.
  
  This article discusses some issues related to medical data, relating specifically to power law distributions and computer aided diagnosis. Read on to see machine learning's place in the puzzle.

  Distribution, Healthcare, Medical, Zipf's Law

• What does a data scientist REALLY look like? - Nov 9, 2018.
  
  Using the responses from Stack Overflow's 2018 Annual Developer Survey, we attempt to build a portrait of data scientists today, including a look at gender, skills, job satisfaction, and more.

  Data Scientist, Developer, StackOverflow

• Multi-Class Text Classification with Doc2Vec & Logistic Regression - Nov 9, 2018.
  
  Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.

  Logistic Regression, NLP, Python, Text Classification

• Deep Learning Performance Cheat Sheet - Nov 8, 2018.
  
  We outline a variety of simple and complex tricks that can help you boost your deep learning models accuracy, from basic optimization, to open source labeling software.

  Cheat Sheet, Deep Learning, Performance

• Best Practices for Using Notebooks for Data Science - Nov 8, 2018.
  
  Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.

  Best Practices, Data Science, Jupyter

• 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
  
  Check out a collection of free machine learning and data science courses to kick off your winter learning season.

  Data Science, Deep Learning, fast.ai, Google, Linear Algebra, Machine Learning, MIT, NLP, Reinforcement Learning, Stanford, Yandex

• Latest Trends in Computer Vision Technology and Applications - Nov 7, 2018.
  
  We investigate the advancements in deep learning, the rise of edge computing, object recognition with point cloud, VR and AR enhanced merged reality, semantic instance segmentation and more.

  Applications, Computer Vision, InData Labs, Trends

• Introduction to PyTorch for Deep Learning - Nov 7, 2018.
  
  In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.

  Deep Learning, Neural Networks, Python, PyTorch

• Text Preprocessing in Python: Steps, Tools, and Examples - Nov 6, 2018.
  
  We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.

  Pages: 1 2

  Data Preparation, NLP, Python, Text Analysis, Text Mining, Tokenization

• Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi - Nov 6, 2018.
  
  Read this post to learn how to build a surveillance system using a USB camera plugged into Raspberry Pi (RPi) which is connected a PC using its wireless interface.

  Pages: 1 2

  Computer Vision, Python, Raspberry Pi, Security, Video recognition

• Mastering the Learning Rate to Speed Up Deep Learning - Nov 6, 2018.
  
  Figuring out the optimal set of hyperparameters can be one of the most time consuming portions of creating a machine learning model, and that’s particularly true in deep learning.

  Hyperparameter, Neural Networks

• Machine Learning Classification: A Dataset-based Pictorial - Nov 5, 2018.
  
  In order to relate machine learning classification to the practical, let's see how this concept plays out, step by step (and with images), specifically in direct relation to a dataset.

  Datasets, Machine Learning, Supervised Learning

• Data Mining Book – Chapter Download - Nov 2, 2018.
  
  Download this immediately useful book chapter, and learn how to create derived variables, which allow the statistical and Data Science modeling to incorporate human insights.

  Data Mining, Data Visualization, Derived Variables, Feature Engineering, JMP, Michael Berry

• Top 13 Python Deep Learning Libraries - Nov 2, 2018.
  
  Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.

  Caffe, Deep Learning, GitHub, MXNet, Python, PyTorch, TensorFlow, Theano

• The Most in Demand Skills for Data Scientists - Nov 2, 2018.
  
  Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. How should data scientists who want to be in demand by employers spend their learning budget?

  Career, Data Science, Data Science Skills, LinkedIn, Python vs R

• Data Science “Paint by the Numbers” with the Hypothesis Development Canvas - Nov 2, 2018.
  
  Now you are ready to take the next step from a Big Data MBA perspective by building off of the Business Model Canvas to flesh out the business use cases – or hypothesis – which is where we can become more effective at leveraging data and analytics to optimize our the business.

  Big Data, Business, Data Science

• Why AI will not replace radiologists - Nov 1, 2018.
  
  We investigate some of the reasons why radiologists will be safe from AI, including the fact that humans will always maintain ultimate responsibility, how productivity gains will drive demand, and more.

  AI, Healthcare, Humans vs Machines

• How Data Science Is Improving Higher Education - Nov 1, 2018.
  
  Increasingly, colleges and universities, as well as governments, are using data science to improve the ways educational institutions do everything from recruiting to engaging with students to budgeting.

  Data Science, Education

• Multi-Class Text Classification Model Comparison and Selection - Nov 1, 2018.
  
  This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.

  Pages: 1 2

  Modeling, NLP, Python, Text Classification